Transportation issues in developing China's urban agglomerations

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Transport Policy journal homepage: http://www.elsevier.com/locate/tranpol

Transportation issues in developing China’s urban agglomerations Hai-Jun Huang a, b, *, Tian Xia a, Qiong Tian a, b, Tian-Liang Liu a, b, Chenlan Wang a, b, Daqing Li c a

School of Economics and Management, Beihang University, Beijing, 100191, China Key Lab of Complex System Analysis and Management Decision, Ministry of Education, China c School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, China b

A R T I C L E I N F O

A B S T R A C T

Keywords: China’s urban agglomeration Transportation system Demand management Risk identification Design and operation optimization Sustainable development

With the rapid development of economy and society, the urbanization process is speeding up in China. There­ upon, several large-size urban agglomerations (or city groups) have emerged. In the present China, a typical city group consists of more than three cities, which are 50–250 km apart from each other, closedly connected in social and economic activities but administratively belonging to different provinces, and each has a population of over five million. It has become an enormous challenge to develop a comprehensive modern transportation system to serve the intercity and intracity traffic effectively, smoothly and eco-friendly. This article presents representative research progress in developing and operating such systems, with consideration of the state condition in China. The following topics, but not limited to, are addressed: (1) Travel behavior analysis and demand integration management within a city group; (2) Agglomeration system design and operation management; (3) Risk iden­ tification and emergency management of the agglomeration system; (4) Sustainable development issues.

1. Introduction It is forecasted that the urban population will exceed one billion by 2025 in China. The new citizens mainly migrate from China’s interior areas. Most of them will settle down in 23 cities with populations over 5 million, where eight of them are with populations over 10 million. It will be an urbanization on an unprecedented scale in the world history. With the rapid urbanization in China, some big cities and even megacities, e.g., Beijing, Shanghai, Guangzhou, Shenzhen, Tianjin and Chongqing, are suffering severe “urban diseases” such as resource shortage, environmental pollution, traffic congestion, and traffic casu­ alty. In order to solve these prominent challenges and problems during the urbanization progress, and to promote the harmonious development of society, China has successively issued various strategies, guidelines and policies in the new-type urbanization and the construction of urban agglomeration, such as “The National New Urbanization Plan (2014–2020)”. The plan emphasizes that China should vigorously develop intercity and intracity railways, aiming to construct a multilevel rail transit backbone network which can effectively connect various large, medium and small cities. Therefore, China will give pri­ ority to public transport and accelerate the development of largecapacity public transport modes such as urban railway transit, bus rapid transit and other green ones. In Beijing, Shanghai and Guangzhou,

the integrated hubs serving a variety of public transport modes, are being built. An urban agglomeration may emerge in various forms. It can be a cluster consisting of several central cities, or a metropolitan circle con­ taining cities of different sizes. It can contain multi-nucleus, doublenucleus, or single-nucleus. In other words, it could be radially or loosely developed. The Jing-Jin-Ji urban agglomeration is shown in Fig. 1 as an example. At present,the extensive and intensive developments of China’s urban agglomeration are under way at the same time. The spatial form, land utilization, industrial layout, population distribution and transportation structure are all simultaneously undergoing great changes, constant adjustment and evolution constantly. This will not only change the temporal and spatial scale of residents’ travel and goods movement in urban agglomeration, but also profoundly affect the hi­ erarchy and coupling relationships between intercity communication and intracity activities. The arrival of big data era provides us a great opportunity to timely acquire comprehensive traffic information in urban agglomeration, explore the demand and behavior characteristics, so as to develop an effective urban agglomeration travel information service and demand management platform. At present, however, the fragmented traffic data and decentralized territorial management restrict the smart and full-information service promotion of urban agglomeration. In the transportation network of an urban

* Corresponding author. School of Economics and Management, Beihang University, Beijing, 100191, China. E-mail address: [email protected] (H.-J. Huang). https://doi.org/10.1016/j.tranpol.2019.09.007 Received 15 June 2019; Received in revised form 21 August 2019; Accepted 28 September 2019 Available online 30 September 2019 0967-070X/© 2019 Elsevier Ltd. All rights reserved.

Please cite this article as: Hai-Jun Huang, Transport Policy, https://doi.org/10.1016/j.tranpol.2019.09.007

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Fig. 1. The Jing-Jin-Ji urban agglomeration and its backbone transportation system. The words ‘Jing, Jin and Ji’, respectively refer to Beijing, Tianjin and Hebei province, which are administratively independent. The capital city of Hebei is Shijiazhuang. The population of Beijing, Tianjin and Shijiazhuang are 21.52, 15.56 and 10.88 million, respectively. Each city shown in the figure, has a population of more than three million.

agglomeration, the spatiotemporal distributions of travel demand, as well as their evolution trends have the characteristics of crossadministrative region, multi-scale, multi-mode, highly stochastic, and dynamical etc, which are much more complex than that in a single city’s transportation network. Thus, to break the geographical limit on data use, it is vital to thoroughly analyze the disaggregate traffic behaviors of huge individuals and the process of producing aggregation behavior within and between cities. Due to the expanding scale of China’s urban agglomeration, the threats posed by various kinds of emergencies and natural disasters to safety are becoming more serious. In the event of emergencies and natural disasters, the comprehensive transportation system plays a key role in evacuating people, transporting disaster materials and advancing post-disaster reconstruction. In addition, an urban agglomeration transportation system has its own systemic risk, i.e., the operation is subject to the disturbance of factors such as extreme weather, unex­ pected emergency and temporary social activity. In a large and complex system, the coupling at network level and the interdependence of different risks increase greatly, which may lead to a larger-scale collapse and failure of the system. For example, a hub is the key node of a transportation system, which serves a huge passenger flow. The reli­ ability of the whole transportation system will be diminished, once an emergency occurs around a hub. Therefore, risk identification and emergency management in the comprehensive transportation system is an important research topic to be considered. Building a “resource-saving and eco-friendly society” is a national strategy for China to achieve sustainable development. The

development of comprehensive transportation system plays an irre­ placeable role in promoting the “new-type urbanization strategy” pro­ posed by China’s central government. The comprehensive transportation system plays a fundamental role in guiding and sup­ porting the spatial form, industrial layout and land use of the agglom­ eration. Undoubtedly, the design and operation optimization of this system rely on multidisciplinary interaction and integration, including resource allocation, coordination organization reforms, multi-mode connection improvement, and the system’s carrying capacity enhance­ ment. The ultimate requirements for constructing such a system include “energy saving, emission reduction, land saving and material saving”, which greatly rely on the development of various green transport modes. Thus, developing public transport, especially rail transport, should be given priority. However, building such a strong intercity railway network is costly, and a lack of capital has constrained its construction. Therefore, we have to coordinate the capital investment of local governments, and stimulate the enthusiasm of market entities through innovating the investment and financing policies, thereby realize the sustainability of trans­ portation infrastructure construction itself. More importantly, the urban agglomeration comprehensive transportation system should adapt to the distribution of population and resource in the region, guide the land use and industrial layout, promote the integration of upstream and down­ stream industries, accelerate the rapid circulation of production factors, and finally form a pattern of regional resource sharing, dislocation competition and integrated development. Therefore, the innovation of investment and financing modes in developing this system is also worth 2

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Fig. 2. The logical diagram of four subtopics contained in Topic (1).

following reviews are conducted from four aspects: multi-scale traffic demand prediction and evolution mechanism, trip chain rule and theoretical model driven by big data, theories and methods for traffic demand guidance, and full-information service and management of smart urban agglomeration. The logical diagram of these four aspects is shown in Fig. 2.

studying. To sum up, taking into account the importance and scientific nature in developing China’s urban agglomeration comprehensive trans­ portation system, we have distilled four topics worth studying, as fol­ lows. (1) Traffic behavior analysis and demand integration management in an urban agglomeration; (2) Design and operation optimization of an urban agglomeration comprehensive transportation system; (3) Risk identification and emergency management of the system; and (4) Sus­ tainable development issues of the system. Each topic further covers four subtopics. The four subtopics in Topic (1) are prediction and evolution mechanism of multi-scale traffic de­ mand, trip chain rule and theoretical model driven by big data, theories and methods for traffic demand guidance, and full-information service and management of smart urban agglomeration. In Topic (2), the four subtopics are transportation hub layout and corridor design in an urban agglomeration, multi-modal transport service and scheduling optimi­ zation, coordinated organizing and sharing of intercity traffic flow, and organizing and operation of urban agglomeration rail transit system. In Topic (3), the four subtopics are risk identification and resilience regu­ lation of urban agglomeration transportation network, information service and management strategy of urban agglomeration emergency transportation, crowd aggregation law and evacuation strategy in transportation hub, and paradigm innovation of emergency manage­ ment in multi-modal transportation network. In Topic (4), the four subtopics are evolution mechanism of spatial form and traffic structure of urban agglomeration, coordinated optimization of comprehensive transportation and industrial development, sustainable development of land use and green transportation, as well as investment and financing mode and decision-making of urban agglomeration transportation infrastructure.This article is organized as follows. For each subtopic, we first review the relevant literature, then present the main research contents or research framework.

2.1. Multi-scale traffic demand prediction and evolution mechanism Traffic demand matrix is one of the most important input data for traffic system management. In practice, various survey-based methods can be used to obtain traffic demand, such as family-based survey, roadside survey, and vehicle license plate recognition technology. These survey-based methods can obtain accurate data through strict enforce­ ment, but are expensive, especially in large networks. It would be economical and effective to estimate the traffic demand with partial traffic flow data. Researchers have proposed various traffic demand prediction methods using partial flow information to infer the global data. These methods include the maximum entropy model, the maximum likelihood model, the generalized minimum variance model, the Bayesian inference prediction model, and the Markov chain model (Shao et al., 2015). Most demand prediction methods using partial traffic flow, assume that traffic demand is a certain variable or mutually independent random variable. Hence, these methods cannot reflect the statistical characteristics of demand correlation. Considering the randomness and correlation of traffic demand, Shao et al. (2014, 2015) proposed a new method to estimate the expected value and variance of traffic demand using partial traffic flow. Given the origin-destination (OD) demand matrix, traffic assignment is used to obtain the link flow by assigning the OD demand onto routes according to some route choice principle. Congestion can be considered when measuring the link travel time or cost. The user equilibrium flow pattern is a state at which all used routes connecting every given OD pair in a traffic network have the identical and minimal travel time or cost (Wardrop, 1952). At this state, no traveler can experience lower travel time or cost by unilaterally changing routes (Sheffi, 1985). To obtain link flow pattern is the basis of conducting travel behavior analysis, network planning and traffic management. Traditional research mainly focuses on estimating the flow pattern in equilibrium state. However, the real traffic is always evolving over time before reaching an equilibrium state. Even if an equilibrium state is reached, the system will become non-equilibrium again due to the disturbance of external information and the change of land use. There­ fore, we should first reveal the dynamic evolution process and mecha­ nism of traffic demand and flow, then develop various accurate prediction models, and finally provide new approaches and methods to alleviate congestion (Guo et al., 2016a). At present, researchers have

2. Traffic behavior analysis and integrated demand management Traffic behavior analysis and demand prediction are the foundation of traffic information service and demand integration management. Traditional trip-based behavior analysis and demand management the­ ories and methods mainly aim at one-way trips completed within a city, while less attention has been paid to the logical relationship and activity connection between trips, as well as the intercity traffic demand. The activity-based trip chain modeling analysis, in which trip is originated from and built on activity, is more suitable for analyzing the formation and evolution of resident travel and cargo transportation demand in an urban agglomeration. Hence, it is expected to propose and develop new theories and new methods oriented to traffic information service and demand integration management in an urban agglomeration. The 3

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Fig. 3. The technical roadmap of multi-scale traffic demand prediction and evolution mechanism.

proposed some dynamic system models to describe the evolution of traffic demands and flows in general road networks (e.g., Friesz et al., 1994; Shao et al., 2014; Guo et al., 2015). The spatial structure of an urban agglomeration is complex. Its transportation is characterized by hierarchical travel distances, diver­ sified travel purposes, combined travel modes and random daily travel demand. Different from a single city, the traffic network in an urban agglomeration has the complex characteristics of multi-scale and vari­ able structure. The number of OD pairs, long distance routes, transport modes and mixed travel chains are large and elastic. Therefore, due to the computational complexity, the demand prediction and flow evolu­ tion models in current literature cannot be directly applied to urban agglomeration traffic system (Huang and Lam, 2005; Zhang et al., 2005; Liu et al., 2015). Therefore, the key scientific issues for this subtopic should include:

Researchers have utilized such disaggregate as Mixed Logit and Nested Logit models to study the transport mode choice behaviors (Lu et al., 2015; Xu et al., 2016) and extended the activity-based traffic assignment to super networks (Liu et al., 2015, 2016). With the development of communication network, GPS navigation system and Internet of Things in a city, various dynamic information associated with people, vehicles and roads can be collected in real time, which can help realize an efficient comprehensive transportation system with intelligent service and intelligent decision. First of all, the tech­ nology about behavior big data makes it possible to obtain travelers’ multidimensional data and carry out statistical analysis. The most direct application of this technology is the prediction of traffic demand in an urban agglomeration, and the establishment of a multi-dimensional travel information integration platform at different levels. Researchers have proposed new models and effective algorithms for multi-mode OD matrix estimation by comprehensively using mobile phone data, loop data, cloud data, and subway electronic toll data (Toole et al., 2015; Ji et al., 2015). Secondly, emerging data sources such as mobile phones and GPS can provide huge volume of data, which are impossible for traditional data sources. More importantly, big data can portray more microscopic traffic behavior characteristics, providing a possibility of conducting more accurate research on trip chains (Calabrese et al., 2013; Chen et al., 2016; Hu et al., 2012; Wang, 2015; Li et al., 2015b). Taxi GPS data can be used to analyze the drivers’ driving and route choice behaviors (Jiang et al., 2009; Liu et al., 2012). Social media data can help researchers analyze the special characteristics of travel behavior against specific locations and times (Cheng et al., 2011; Noulas et al., 2012). In addition, the popularity of new information technologies such as smart phones and WeChat social groups has also changed human travel behavior, especially the decision-making process of travel plans. Existing re­ searches have attempted to analyze the travel behavior characteristics in

(a) Modeling and predicting the daily activity travel plans of in­ dividuals under different scales. (b) Sharing and integrating multi-mode and multi-source travel in­ formation, and designing the algorithm inversely deriving trip chain demand. (c) Revealing spatial and temporal evolution laws and trends of multi-scale traffic demand. The technical roadmap of this subtopic, including the methods used, the conduction process and the detailed questions, is shown in Fig. 3. 2.2. Trip chain rule and theoretical model driven by big data Research on activity-based trip chain depicts the choice and sequence of residents’ social activities, gives explanation to trip gener­ ation from behavioral perspective, thus is coherent and dynamical. 4

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Fig. 4. The roadmap of studying trip chain rule and theoretical models driven by big data.

specific contexts (e.g., work and leisure) using social network data and travel records (Wall et al., 2014; Ruiza et al., 2016). In general, existing methods for trip chain analysis and measures for travel demand management cannot fully adapt to the development requirement of urban agglomeration comprehensive transportation system under the new-type urbanization guidance. It is thus necessary to organically integrate model-driven and data-driven researches. Due to fragmented traffic data and territorial management restriction, current big data-based researches on trip chain rule and modeling approach mainly focus on travel activities for work, school, shopping and enter­ tainment within one city. There is a lack of in-depth studies on the dif­ ferences of activity characteristics and user heterogeneity for urban agglomeration comprehensive transportation system. Furthermore, there is still no effective data sharing and collaborative governance mechanism for integrating the urban agglomeration comprehensive traffic big data. The key scientific issues of this subtopic should include:

(b) Developing a disaggregate travel decision-making model under different scales within an urban agglomeration. (c) Proposing a dynamic, activity-based, combined trip distribution and traffic assignment model in the network of urban agglomeration. The roadmap of studying trip chain rule and developing theoretical models driven by big data is shown in Fig. 4. 2.3. Theories and methods for traffic demand guidance Understanding the characteristics of traffic demand within and be­ tween cities is an important prerequisite for efficient traffic demand management in urban agglomerations. Since the 1970s, the research focus has shifted from transportation infrastructure construction to traffic demand management. After nearly half a century, relevant the­ ories and methods of traffic demand management have achieved rapid development. In recent years, the use of price and market mechanism for traffic demand guidance has become one of international research hot­ spots, e.g., bus priority (Xu et al., 2010), parking charge (Liu et al., 2009; Xu and Grant-Muller, 2016), congestion toll (Yang and Huang, 1998,

(a) Revealing the activity law of chain trip behavior driven by big data.

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Fig. 5. The roadmap of studying the theory and method for traffic demand guidance.

2005; Bao et al., 2015; Guo et al., 2016a), tradable credit scheme (Yang and Wang, 2011), and carpooling on HOV lanes (Xiao et al., 2016). The bus priority policy and parking charge aim to guide the traffic demand from private mode to public mode by improving the service level of bus system and implementing reasonable economic regulations. Xu et al. (2010) specifically studied the mechanism of bus priority such as bus fare subsidies, bus lanes, and bus service levels to alter the traffic demand in cities. Xu and Grant-Muller (2016) investigated the mid-term and long-term impacts of private car ownership management, staggered peak hours plan, parking charge policy, and vehicle restriction based on license plate numbers. Liu et al. (2009) studied the parking charges in a multi-modal transportation system with continuously distributed par­ king/transfer points. Economic measures such as congestion tolling can effectively regu­ late traffic demand. Various toll strategies, e.g., time period-based charging, continuously dynamic charging, and anonymous charging, will significantly affect the transport mode choice, route choice and departure time decision, leading to different system costs (Yang and Huang, 1998, 2005). Bao et al. (2015) explained why the traffic flow on the tolled path is always overestimated by introducing the concept of psychological account and psychological budget. Guo et al. (2016a) proposed a price-based dynamic congestion control strategy to analyze the evolution process of road traffic, which proved that the traffic flow and charge eventually evolve into a Nash equilibrium state through a game between managers and travelers. Congestion tolls may cause unfairness in society. Alternatively, Yang and Wang (2011) proposed a new measure of tradable credit scheme to alleviate urban traffic congestion. It can achieve the same effect as congestion tolling, with the advantages of flexible management and fair distribution of gains and losses. Attempts on traffic demand guidance have also been made on rail transit. Yang and Han (2000) compared the traffic demand character­ istics of four international metropolises, i.e., New York, Tokyo, London

and Paris, and found that their rail transit systems play an important role in guiding traffic demand. They also raised several basic issues that must be considered when building similar rail transit systems in China’s metropolitan areas and urban agglomerations. Li et al. (2016b) used the urban agglomeration of Yangtze River Delta as an example and studied the impact of high-speed railway on traffic demand and economic ac­ tivities. Li et al. (2017) discussed the hierarchical allocation of trans­ portation hubs in an urban agglomeration, and designed a hub-based hierarchical service network for the studied region. To sum up, the existing researches on theories and methods of guiding traffic demand are mostly oriented from a single city, lacking indepth studies on the differences of activity characteristics within and between cities or towns, as well as travel behavior heterogeneity. Thus, these theories and methods cannot fully adapt to the comprehensive structure of an urban agglomeration. Management schemes such as road-use restriction, private car purchase restriction, congestion toll, tradable credit scheme and other congestion control measures were designed for managing the demands in a specific urban area, while travel demands from other areas were excluded. This clearly contradicts the requirement of coordinated developments in urban agglomerations. Thus, we have to develop new theories and new methods for compre­ hensively guiding the traffic demand in urban agglomerations. The key scientific issues of this subtopic should include: (a) Studying the functional mechanism and management applica­ bility of quantitative control and market regulation strategies on traffic demand of urban agglomeration. (b) Establishing a set of integrated management theories for urban agglomeration traffic demand that takes both fairness and effi­ ciency into account. (c) Proposing the traffic demand guidance (inducement) strategy and management method for sharing mobility.

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Fig. 6. The roadmap for studying full-information service and management for smart urban agglomeration.

The roadmap for the above mentioned studies is shown in Fig. 5.

vehicles, roads, environment and management agencies. In this system, each individual is a data collection point contributing his/her own travel experience information, meanwhile a service object receiving the fullinformation of the system. This system matches the demand and resource, integrates the service and management in the context of the mobile Internet era (Wang et al., 2016). At present, research of this system stays at the smart city level, not the urban agglomeration level. In summary, the construction of a smart urban agglomeration re­ quires a complete intelligent transportation system, including the cor­ responding service system and management policies. The rapid development of information technology (Internet of Things, cloud computing, big data) makes the full-information traffic system gradually become the center of the whole intelligent transportation. This system can serve the diversified and personalized traffic demand in the era of mobile internet, hence is a hot research topic. The key scientific issues of this subtopic should include:

2.4. Full-information service and management of smart urban agglomeration With the development of China’s information technology (Internet of Things, cloud computing, big data), research on full-information service and management of smart urban agglomeration should provide conve­ nient, reliable and friendly services, and make true the intelligent management of urban agglomeration integrated transportation system. At present, relevant researches are mainly concentrated on the following two aspects. The first is the relationship between smart urban agglomeration and smart transportation. Smart urban agglomeration is a new concept based on the application of information technology in smart cities and urban agglomerations, which is the higher stage of the integration of smart city construction and urban agglomeration development. The concept of smart urban agglomeration stems from the study by Matthews (1996). He believes that electronic information will fill in gaps between and within cities, supporting all aspects of urban life, including energy, health care, education, and transportation. Smart transportation will play an important role in the development of smart urban agglomera­ tions. On one hand, smart urban agglomeration will drive the develop­ ment of smart transportation. On the other hand, smart transportation can provide services to all individuals and groups within an urban agglomeration to significantly improve travel efficiency, ensure traffic safety, and enhance energy efficiency. This relies on a real-time, accu­ rate, efficient and safe integrated traffic management system. Relevant practices are gradually carried out in China and abroad, for example, the “Smart Country 2025” plan in Singapore, “I-Japan” stra­ tegic plan in Japan, and the “Smart Yangtze River Delta” in China’s Southern Jiangsu (Cheng, 2013). However, these practices are limited to build smart cities or regions, not yet urban agglomerations. In addition, these studies and practices emphasize the technologies of information, communication and navigation, but somewhat neglect the service and management to them. The second is the full-information service. The rapid development of information technology has provided a strong guarantee for the con­ struction of smart urban agglomeration. The full-information system realizes the online data interaction and sharing between people,

(a) Constructing an index system used for evaluating the fullinformation travel service within a smart urban agglomeration. (b) Developing the traffic demand management theories driven by full-information travel service of smart urban agglomeration. (c) Proposing traffic management strategies for different functional levels, structural characteristics and spatial circles of urban agglomeration. Fig. 6 depicts the roadmap for studying full-information service and management for smart urban agglomeration. 3. Design and operation optimization The huge traffic demand caused by the new urbanization features high intensity, diversification, high frequency and strong timeliness, which brings new challenge to the transportation system. Developing a multi-level and multi-mode transportation network has been regarded as an effective way to solve this problem. The design and operation of urban agglomeration comprehensive transportation system is a complex system engineering, which needs to take into account a variety of fac­ tors, and study the interactive influence mechanism between spatial layout and transportation network. Existing studies on the design of traffic system mainly focus on general urban traffic networks, while few 7

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Fig. 7. The logical diagram of four subtopics contained in Topic (2).

consider the design and operation optimization of urban agglomeration comprehensive transportation system. Rail transit system, as the back­ bone of urban agglomeration transportation system, is important driving force for the development of China’s urban agglomeration. However, this system needs redesign through optimization, specially increasing the connectivity within an urban agglomeration. Design and operation optimization of urban agglomeration comprehensive transportation system need to comprehensively consider the overall coordination of internal and external transport modes of cities from the macro perspective, including four aspects: transportation hub layout and corridor design of urban agglomeration, multimodal transport service and scheduling optimization, coordinated organizing and shared loading of intercity traffic flow, and organizing and opera­ tion of urban agglomeration rail transit system. Hub layout and corridor design constitute the foundation for studying multimodal transport service and scheduling optimization. The logical diagram of these four

aspects is shown in Fig. 7. 3.1. Hub layout and corridor design of urban agglomeration The topic of transportation hub location and layout has been well studied in the literature. Yama (2009) proposed a mixed integer pro­ gramming model for designing a three-level freight hub network, and applied this model in Turkish. Ishfaq and Sox (2011) developed a freight hub location model of the multimodal transport logistics network, considering the transportation cost, mode connection cost and facility investment. The Lagrange algorithm was used to solve this model. Alumur et al. (2012) investigated a hierarchical layout optimization problem containing highway and aviation hubs with time constraint on goods delivery, in which the allowance of direct transportation between non-hub nodes was considered besides the fixed and variable costs of hub connection. Yang et al. (2016a) addressed the impact of uncertain

Fig. 8. The roadmap for studying hub layout and corridor design for urban agglomeration.

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Fig. 9. The roadmap for studying multimodal transport service and scheduling optimization.

information in hub location, formulated an optimization model with uncertain constraints, and employed the simulated annealing algorithm to solve large-size problems. Yang et al. (2017b), proposed a two-objective optimization model for designing the hub-and-spoke network, which simultaneously minimizes the transportation time and cost. Obviously, the concept of agglomeration must be emphasized when studying the China’s hub layout. Li et al. (2016a) formulated an opti­ mization model for urban agglomeration comprehensive passenger transport hub location, exploring the significance and importance of hierarchical hub layout. The model was solved by software Cplex and validated by real data. Optimizing the design of a transportation corridor is also a critical topic, because the corridor constitutes the kernel of the transportation system. Wirasinghe and Seneviratne (1986) studied the relationship between corridor length and transportation cost in a rail transit system, formulated and solved the proposed optimization model. Lam and Zhang (1998) investigated the joint optimization problem of corridor layout and capacity design. Li et al. (2012b) optimized the density of stations along a rail corridor, aiming to maximize the corridor system’s revenue. Hsu and Chung (1997) studied the modal split problem be­ tween existing railways and to-be built high-speed ones, considering the passengers’ choice behavior, and optimized the layout of transport corridors. Liu et al. (2010b) developed a three-level programming model to optimize the layout of regional transport corridors, in which the benefits and costs of government, transportation enterprise and goods owner or passengers were involved. This model was verified using the data of Beijing-Tianjin-Hebei region. However, most of existing studies focused on hub location only, and few studied the hierarchical layout optimization of comprehensive transportation hubs in the context of urban agglomeration. In addition, current studies mainly analyzed the micro channel or corridor of a single transport mode, lack of systematic research on the design theory of

urban agglomeration comprehensive traffic corridors. Here, we list the following key scientific issues for further study: (a) Location and hierarchical layout optimization of comprehensive transportation hubs in an urban agglomeration. (b) Corridor design and function determination methods applied in urban agglomeration comprehensive transportation system. (c) Coordinated optimization of hubs and corridors in urban agglomeration comprehensive transportation system. The roadmap for studying this subtopic is shown in Fig. 8. 3.2. Multimodal transport service and scheduling optimization With the support of comprehensive transportation system, in China, a new urban system or spatial pattern which contains one to three central cities, several peripheral cities and some new towns, has emerged. In the central city, it is necessary to improve the multi-level and multi-modal transfer and connection function for public transportation. Wong et al. (2008) established a mixed integer programming model, minimizing the passenger waiting time at railway station. Wu et al. (2015) proposed a bi-level programming model to minimize the pas­ senger travel time and balance the road network service capability. Huang et al. (2017) developed a mathematical model to resolve the poor safety and low efficiency of transferring passengers at bottleneck rail­ way stations in big cities. Guo et al. (2017) proposed a connection model to increase the transfer accessibility and reduce the passenger time consumption and railway operation cost. Most urban rail transit net­ works belong to central radioactive structure, the connection problem of circular lines is worth studying (Shi et al., 2016a). As a result of grad­ ually expanding the rail transit network, the accessibility in peripheral 9

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Fig. 10. The study map for coordinated organizing and shared loading of intercity traffic flow.

city areas is becoming high and the passenger transfer service is also high. Kang et al. (2015b) analyzed the transfer and connection problem through optimizing the departure times of the last trains of all rail lines. They proposed a tolerance time model to dispatch the last trains, which improved the transfer and connection reliability of passengers at all stations. Wu et al. (2016) studied the transfer and connection problem in bus networks, adopting a multiple objective programming model. In the research of Shanghai’s urban agglomeration transportation, Chen (2017) suggested to optimize the design of a multi-level rail transport system from downtown to metropolitan area, and pointed out that the coordinated development of multiple transport modes and the formation of chain-type transfer network, are of great significance to cities. They put forward that integrating different transport modes and improving transfer service will contribute to the benign development of urban traffic. In the future, great efforts should be made to improve the inte­ gration of various transport modes, thereby realize a comprehensive transportation system with multiple scales, multiple modes, multi-levels and less time consumption. The key scientific issues of this subtopic include:

3.3. Coordinated organizing and shared loading of intercity traffic flow Intercity traffic is the foundation of developing urban agglomeration, as it is the main carrier of passenger and freight transport between cities. Exploring the evolution law of intercity traffic flow and grasping the operation rule of intercity transportation system, are the indispensable bases for well planning, managing and controlling the system. Tradi­ tional traffic flow theory focuses on the micro-level analysis of local flow, using the simple relation between density and capacity, which ignores the conflict impact of different traffic modes. However, a large number of data show that traditional models can no longer fully reflect the macro characteristics of traffic flow due to the complicated structure of urban transportation. The network characteristic of traffic flow draws forth the demand for modeling traffic flow at macro or network level. Therefore, studying the operational state of a network based on the unit of traffic zone has become a hot spot. Daganzo (2007) stated that in the urban road network, a macro dynamic model could be developed for areas with similar traffic congestion condition. Geroliminis and Daganzo (2008), using the field data analysis, found that there is a strong correlation between the cu­ mulative number of vehicles and the traffic capacity of the studied area. They defined it as Macroscopic Fundamental Diagram (MFD), and pointed out that the MFD can reflect the macro physical property of a large size road network. The emergence of MFD theory provides a theoretical basis for traffic flow organizing optimization at network level. Since then, diversified control and optimization strategies based on the MFD have been reported in the literature (Zheng et al., 2012; Aboudolas and Geroliminis, 2013; Yang et al., 2017c; Ampoundolas et al., 2017). Ji and Geroliminis (2012) and Ji et al. (2014), considering

(a) Optimally matching the supply capacity and demand flow of different transport modes in an urban agglomeration. (b) Transfer organizing and coordinated management of various transport modes in an urban agglomeration. (c) Connection optimization and scheduling strategies of urban agglomeration comprehensive transportation network. The roadmap for conducting these studies is shown in Fig. 9. 10

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Fig. 11. The roadmap for organizing and operation of urban agglomeration rail transit system.

the heterogeneity of static traffic flow, proposed a partition method for addressing the largest similarity of traffic state within a region and the smallest similarity between regions, and proposed corresponding con­ trol strategies for these regions. These explorations focused on the in­ ternal urban traffic network, but rarely on the evolution law and organizing method of intercity traffic flow. Given the huge scale of transportation infrastructure in urban agglomeration, and diversified information sources of traffic flow, it is essential yet still unknown how to make use of these resources and realize the efficient resource sharing for improving the overall transport service level. To sum up, the deficiency of existing theories has seriously restricted the accurate awareness, effective organizing and real-time optimization of intercity traffic flow and multi-mode transportation system. The is­ sues concerning the characteristic and operation organizing of urban agglomeration traffic flow are worth studying. The key scientific issues of this subtopic are as follows.

Zhao et al. (2014) proposed a model to coordinate the passenger flows between railway stations along a single line or in a network. Tsuchiya et al. (2006) studied the passenger flow information release problem with an assumption that the train operation is interrupted in Tokyo subway. The impacts caused by operation interruption events can be quickly estimated according the experience from historical data. Yang et al. (2017a) developed a two-stage method to optimize the passenger flow of trains and the connected buses that pick up passengers from trains. In the field of train timetable optimization, scholars have achieved fruitful works. Vansteenwegen and van Oudheusden (2006, 2007) studied the scheduling problem of delayed trains for minimizing the waiting time of passengers. Sun et al. (2014) developed a time-discrete optimization model of train timetable, using the swiping card data and taking into account the train capacity limitation. Yin et al. (2017) pro­ posed an integrated optimization method to reduce the operation cost and passenger waiting time. Guo et al. (2016b) studied the coordination optimization problem of early trains in a rail transit network. Ye and Liu (2016) investigated the problem of collaboratively scheduling the multi-period trains. Kang et al. (2014, 2015a) investigated the last train problem, developed a mathematical optimization model and heuristic solution method, and conducted a case study in Beijing. Yang et al. (2016b) optimized the train timetable with energy consumption mini­ mization. Wu et al. (2015) minimized the total waiting time of all pas­ sengers who need to transfer between different railway lines. To sum up, the existing studies mainly focus on the optimization of passenger flow using a single strategy, or the optimization of train timetables subject to a single transport mode. It will be the trend for future research to adopt a data-driven approach, consider complex dy­ namic passenger flow and employ combined optimization strategies, to finally realize efficient allocation of various resources in the urban agglomeration rail transit system. The key scientific issues of this subtopic include:

(a) Evolution mechanism and modeling of multi-region macroscopic traffic flow in an urban agglomeration. (b) Collaborative operations organizing and regional inducement optimization methods of intercity traffic flow. (c) Resource sharing in a mixed traffic system with multiple regions and multiple modes. Fig. 10 depicts the roadmap for studying coordinated organizing and shared loading of intercity traffic flow. 3.4. Organizing and operation of urban agglomeration rail transit system In an urban agglomeration rail transit system, the city rail transit, suburban railway and intercity rail transit give full play to their own unique advantages in providing multi-level choices and diversified ser­ vices for urban agglomeration residents, meanwhile, they work complementarily and collaboratively. As for the organizing and opera­ tion management of urban agglomeration rail transit system, systematic and in-depth research has not yet been carried out in China. Current studies mainly focus on the organizing of rail passenger flow and the optimization of train timetable.

(a) Accurate estimation of capacities of stations, lines and networks within an urban agglomeration rail transit system. (b) Dynamic resource allocation and organizing optimization of the rail transit network driven by data. 11

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Fig. 12. The logical diagram of four subtopics contained in Topic (3).

Fig. 13. Operation bottlenecks in a traffic network (Li et al., 2015a).

(c) Optimization of the multi-period and multi-objective operation timetables used for an urban agglomeration rail transit system.

4.1. Risk identification and resilience regulation of urban agglomeration traffic network

The roadmap is given in Fig. 11.

Urban agglomeration as a large network system with multiple traffic modes, has a complex process of accumulating operational risks. It is more difficult to be predicted than that inside a single city. Rather than avoiding the underlying risks in traffic operations, for urban agglom­ eration, resilience is a more practical view to enhance the adaptation and recover ability of the transportation system (Zhang et al., 2019). Therefore, new resilience methods are required to identify and manage the hidden risks from urban agglomeration transportation system. The key for identifying risks is to accurately estimate and predict the traffic network state, including the congestion identification based on model simulation and data analysis, e.g., the use of Kalman filtering method (Wang and Papageorgiou, 2005). The early traffic condition estimation mainly relies on the monitoring data (Gazis and Knapp, 1971), which is mainly suitable for the case with short distance and small range traffic. The later research started to use dynamic flow model, with parameter estimation technique, to predict the traffic con­ dition over a long distance (Ashok and Ben-Akiva, 2000). While most of the studies is in the view of highway traffic, a small amount of research considers the network traffic. Zheng et al. (2014) integrated the different types of traffic data to warn congestion risk in a network. Existing studies seldom consider the propagation of congestion risk within a large-scale network, which hinders the ability to identify the operation risk at network level. At the network level, the congestion propagation is spatially correlated (Li et al., 2014) and dynamically circularly spreading (Zhao et al., 2016). Using the percolation theory, Li et al. (2015a) analyzed the dynamical organizing process of urban traffic, and proposed a method to identify the key bottlenecks, as shown in Fig. 13. In view of urban agglomeration traffic network, we should develop more task specific methods for identifying various risks. On the basis of effective identification of traffic operation risks, regulating the operation state is one of the core problems to realize the scientific emergency management of traffic system. Existing regulation methods can be classified into local and global types. Local regulation refers to adjusting local traffic resources to realize the guidance and control of local traffic flow, such as setting the roundabout at intersec­ tion, and conducting intersection reservation control (Dresner and Stone, 2004). Global regulation guides travelers to change their travel strategies in space and time, even transport mode, through optimizing traffic lights (Gao and Song, 2002), pricing road use (Guo and Huang, 2007), pricing for parking (Tian et al., 2018), as well as implementing other measures. On the other hand, some scholars proposed the general framework for analyzing the controllability of complex network systems (Liu et al., 2011) and the method for target control (Gao et al., 2014a), with few attentions on the specific organization feature and operation risk of the traffic network. Correspondingly, Zhang et al. (2019) has developed a new resilience definition for transportation system,

4. Risk identification and emergency management Urban emergency management has been put forward by the New York City RAND Institute in 1966 for more than 50 years (Green and Kolesar, 2004). At present, this theory has been widely applied in all aspects related to cities, including urban transportation. The develop­ ment of urban agglomeration is also inseparable from the application of emergency management theory. Urban agglomeration traffic is different from the traffic in a single city, which is a complex network system containing a variety of trans­ port modes and spanning multiple cities. Identifying and further con­ trolling the system’s internal risks has gradually become a research hot spot. Li et al. (2015a) proposed a method to identify dynamical bottle­ neck spots in an active traffic network. Gao et al. (2016) showed that this complex network system may have a universal resilience pattern. Hsu (2010) conducted a new research on urban agglomeration traffic, which formulated all travel of an individual as a complete trip chain, analyzed the correlation, coupling and preference of the travel, and found the reasons for mistakenly forecasting traffic demand. Hub safety is very important for keeping the overall stability of the traffic system in case of emergency. The issue of crowd evacuation around transportation hubs is a research hot spot in the field of emer­ gency traffic management. Helbing et al. (2000) developed a social force model to depict the individuals’ evacuation behavior. Gao et al. (2014b) studied the pedestrian route choice problem in evacuation, for opti­ mizing the hub location and evacuation plan. Previous emergency traffic management usually limited the prob­ lems to a single traffic mode, such as highway, railway and so on, while few studies took multiple traffic modes into comprehensive consider­ ation. This provides an opportunity to develop new theories and new methods for urban agglomeration emergency traffic. Chen and Miller-Hooks (2012) was one of a few studies that address the emer­ gency traffic issue in a multiple mode system. To sum up, the study on emergency management of urban agglom­ eration comprehensive transportation system should focus on the following hot spots: to regulate and improve the system resilience on the basis of effectively identifying various risks, to generate multidimensional management strategies on the basis of data science, to develop new evacuation models for hub pedestrians, and finally to innovate the management paradigm for multiple traffic mode system under emergencies. The logical diagram of these hot spots is shown in Fig. 12.

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Fig. 14. The study map for risk identification and resilience regulation of urban agglomeration traffic network.

considering the spatio-temporal behavior of network adaptation and recover from congestion. This is of enlightening significance to urban agglomeration emergency traffic management. To sum up, traffic congestion laws in multi-mode and multi-scale urban agglomeration are essentially unknown. As a result, the existing risk identification and resilience regulation methods developed for a single city can hardly support urban agglomeration traffic emergency management. The key scientific issues contained in this subtopic are as follows.

4.2. Emergency information service and management strategies of urban agglomeration Traffic information system aims at making full use of modern com­ puter, electronic communication, geographic information system and automatic control technology to reduce traffic congestion, enhance traffic capacity, improve traffic safety, and mitigate the negative impact of traffic on environment. The experiences already obtained show that traffic guidance system has a broad application market, and its suc­ cessful development can also drive the developments of electronic, in­ formation, communication and automobile industries. Since 1980s, intelligent transportation systems (ITS) have been implemented in the United States, Japan and Europe, and 1990s was the peak of developing intelligent transportation, especially traffic naviga­ tion systems. The ITS association of the American had pointed out that ITS will become the mainstream of transportation development in the 21st century, and this system may increase the utilization rate of existing roads by 15%–30%. It has been recognized that the traffic guidance system represents the largest potential market of modern high-tech in­ dustry (e.g., information technology), and has a broad market applica­ tion prospect. The promotion of traffic guidance system can not only

(a) Multi-scale propagation law of traffic risks in an urban agglom­ eration transportation system. (b) Determining the resilience limit for an urban agglomeration transportation system. (c) Identifying the operation bottlenecks for an urban agglomeration transportation system. Fig. 14 presents the study map for risk identification and resilience regulation of urban agglomeration traffic network.

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bring significant convenience and efficiency to the public, but also greatly increase the added value of information industry. Khattak et al. (1991) analyzed the influence of travel information on departure time choice using the ordered Probit model and Logit model. They showed that 40% of respondents would not adjust their departure time according to the pre-trip information, and the accuracy and time­ liness of information are the main factors influencing decisions. Based on a questionnaire survey, Khattak et al. (1999) further revealed that the economic characteristic of drivers, information propagation technology and information accuracy are the main factors. Jou (2001) conducted a similar study using the binomial Probit model. Dziekan and Kottenhoff (2007) carried out an empirical study and found that the real-time bus arrival information can reduce the waiting time of passengers at bus stations and assist them to determine departure time from home. Xiao and Lo (2014) employed the Bayesian network learning model to study the departure time choice problem with the help of social media network information. The development of ITS in China has experienced a long process of understanding and accepting this technology. It has been officially launched since the year 1999. In that year, the National Research Center for Intelligent Transportation System was established. Since then, ITS has been listed as one key field to be developed, with a statement “the recent focus of the ITS industrialization is to accelerate the de­ velopments of advanced traffic management system (including traffic guidance system) and traffic information service system, to realize the overall capacity of system design, equipment manufacturing, project construction, system operations and management”. In Shanghai, the elevated road information guidance system, the bridge tunnel operation monitoring system, and the highway operation command system have been put into use. In Beijing, a relatively complete comprehensive traffic information network system, the traffic command and scheduling sys­ tem, the traffic law enforcement system, and the office automation system have been established. In Guangzhou, projects or systems such as the bus information platform, bus positioning and scheduling system, and parking guidance system, are under construction. Chongqing, Jinan, Shenzhen, Qingdao and other cities are also developing their own ITS systems. However, the development of China’s ITS places too much emphasis on engineering technology and infrastructure construction, yet ignores relevant theoretical research. For example, the research on the impacts of traffic information guidance on travel behavior stays on concept and framework, lacking in-depth theoretical understanding. This will affect the application validity of a large number of traffic information systems. In addition, many cities have installed advanced data acquisition sys­ tems, but without utilizing these data to support management decision. As a result, the variable message signs (VMS) installed on some roads cannot release real-time and dynamic traffic guidance information. With the deepening of new urbanization, the city groups represented by the Yangtze River Delta, Guangdong-Hong Kong-Macao Greater Bay Area and the Beijing-Tianjin-Hebei Region are rising rapidly. Some scholars have examined the emergency commanding and management strategies from the perspective of public security in urban agglomeration (Guo and She, 2016). Zhu (2012) applied the super network theory to analyze the interaction between disaster risk environment and dynamic deployment of emergency resources. Lv (2010) introduced the cross-state emergency management coordination system and framework of the United States. However, there are few researches on urban agglomeration emergency transportation. In the event of unpredictable emergencies in an urban agglomeration, it is a must to provide necessary traffic information to travelers in order to prevent the spread of accidents/events and ease traffic congestion timely. Therefore, it is necessary to study the emergency traffic information service and management strategies for urban agglomeration, and analyze the behavioral response rules of individuals and traveler groups, so as to facilitate the incident dissipation. The key scientific issues contained in this subtopic are as follows.

(a) Division and cooperation mechanism of traffic information acquisition and sharing between cities. (b) Traffic flow characteristics caused by travelers’ group behavior. (c) Propagation and diffusion law of traffic flow under information service. (d) Releasing method, location and timing of traffic incident information. 4.3. Crowd aggregation law and evacuation strategies of transportation hub At present, researchers mainly focus on the issues of pedestrian traffic flow in the evacuation process. Evacuation has two scenarios, one under normal state and the other under panic state. The former is the process that pedestrians leave after finishing social activities. The latter is the process that pedestrians have to leave in time in the case of an emergency. For both types of evacuation, an important index is the evacuation efficiency, which measures how quickly pedestrians leave the activity site. In the process of evacuation, if a large number of pe­ destrians are stranded in some sections or places, it is more likely to lead to accidents. Especially when emergencies occur in large, and layout complex places, invalid flow assignment may lead to some pedestrians straying into dangerous areas, resulting in group death and injury. The methods studying pedestrian flow assignment can be divided into two classes, i.e, the empirical and modeling ones (Helbing et al., 2003; Guo et al., 2012; Gao et al., 2014b). The empirical method is based on observation and experiment, and is mainly used to obtain the specific pedestrian data for model parameter calibration, and provide references for walking space design and evacuation scheme formulation. The modelling method can be further divided into the network model (Pursals and Garzon, 2009; Wagoum, 2012), the fluid model (Hughes, 2002; Huang et al., 2009) and the micro model (Hoogendoorn and Bovy, 2004; Guo et al., 2013). The micro model is popular in past years, in which pedestrians are handled as discrete individuals and their move­ ments are depicted by cellular automaton (Burstedde et al., 2001), lat­ tice gas (Muramatsu et al., 1999), or social force rules (Helbing et al., 2000). Each of these three models has advantages and disadvantages. The computational burden of network model is small, but it is difficult to give accurate prediction and detailed information of pedestrian flow in places with obstacles, as well as the walking behavioral characteristics of evacuation pedestrians. The fluid or micro model is not applicable to the assignment of pedestrians in large and layout complex places due to computational limit. Guo et al. (2011) and Haenseler et al. (2014) developed the so-called mesoscopic models which integrates the ad­ vantages of network model and micro model. In this mesoscopic model, corridor, sidewalk and part of walking space are not handled as nodes in the network, but are divided into triangular, square or hexagonal lattices. In reality, a complete social activity includes not only the process of pedestrians leaving the activity site (evacuation process), but also the process of pedestrians entering the site (aggregation process). In the process of pedestrians entering the site, there is also the problem of flow assignment. The processes of pedestrian evacuation and aggregation are interrelated and interacted with each other. Up to now, there have been few studies on the aggregation process of pedestrians (Tang et al., 2012; Feng and Miller-Hooks, 2014). Bad aggregation can also cause crush and stampede accidents. Hereby, we list the key scientific issues contained in this subtopic, as follows. (a) Crowd aggregation process and law around transportation hubs. (b) Distribution characteristics of crowd aggregation under the in­ fluence of information. (c) Influence of crowd aggregation on evacuation around trans­ portation hubs. 14

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(d) Pedestrian evacuation strategies under different crowd aggrega­ tion modes. 4.4. Paradigm innovation of traffic emergency management In the cases of emergencies and disasters, efficiently evacuating people and property to safe areas through traffic network is the key for traffic emergency management. Lee et al. (2006) proposed a network model to determine the safe areas and optimal paths for evacuation. For the evacuation of unforeseeable disasters, Chiu et al. (2007) developed a mixed integer programming model based on network flow. Saadatser­ esht et al. (2009) proposed a network-based multi-objective evacuation model, which takes into account the distance and capacity of reaching safe places. Hong et al. (2015) designed a traffic network to respond to a variety of natural disasters. Recently, Dalal and Uster (2018) put for­ ward the design problem of a random emergency evacuation network, which simultaneously optimizes the crowd evacuation and the trans­ portation of emergency supplies. Due to the dynamics, complexity and uncertainty of the environment under emergency, as well as the psychological factors of pedestrians’ survival behavior, it is difficult to collect the real data associated with evacuation time and route. The traditional static mathematical model is not capable of solving these real evacuation problems. On this occasion, the computer simulation technology has started to be used in emergency traffic research. Balakrishna et al. (2008) presented a simulation framework for emergency traffic network management, and recom­ mended the simulation software DynaMIT. Xie et al. (2012) applied the cellular automaton to simulate the evacuation of indoor pedestrians. Guo and Huang (2012) conducted a micro simulation to study the pe­ destrians’ route choice problem using the potential energy field concept. However, at present, there are few applications of simulation methods in large scale, and complex evacuation networks, particularly the intercity and multi-mode networks. Although emergencies and disasters have serious impacts on comprehensive transport system, previous studies mainly focused on single transport mode, while the collaboration of multiple modes (e.g., highway, railway, aviation and waterway) has not been completely highlighted. Chen and Miller-Hooks (2012) considered the emergent linkage of multiple transport modes (highway, railway and aviation) when solving the problem of container transport after disasters. They constructed a multi-layer network, in which each layer represents a single transport mode, and set links to connect these layers. Recently, Sun et al. (2017) proposed a method to quickly evacuate airport pas­ sengers after natural disasters, through unifying five transport modes (private car, taxi, bus, subway and light rail). Therefore, the researches should focus on deeply integrating multi­ ple transport modes, greatly improving their operation coordination, and finally realizing the quick recovery of urban agglomeration comprehensive transport system. Therefore, the key scientific issues contained in this subtopic include:

Fig. 15. The logical diagram of four subtopics contained in Topic (4).

and reconstruction of the urban area. On the other hand, the spatial form, industrial distribution and land use pattern of urban agglomera­ tion are key factors affecting the spatiotemporal distribution of traffic demand on the network and thus influencing the investment decisions of transportation infrastructure. Fig. 15 shows the logical diagram of four subtopics in this topic. 5.1. Evolution mechanism of spatial form and transportation structure Regarding the evolution of urban agglomeration spatial form, Friedman (1967) proposed a four-stage evolution model which com­ bines the economic growth stage theory and the growth pole theory. These four stages include: pre-industrialization, transition, industriali­ zation and post-industrialization. Lu (1995) presented a “point-axis” model based on the central place theory. Wei (2001) suggested a network form for the space layout. Other scholars have also conducted in-depth studies on the spatial form and evolution law of urban agglomeration by considering realistic examples (Yokohari et al., 2000; Ma and Sun, 2005). By studying the functional mechanism between transportation and urban spatial form evolution, scholars have fully affirmed the important role of transportation in the formation of urban agglomeration. Liu (2010) pointed out that Tokyo’s rail transit construction and the spatial form evolution are coupled for development. Chen et al. (2011) studied the impact of high-speed railway between British cities on urban rent and industries, and analyzed the influence of transportation infrastruc­ ture on spatial form evolution of the urban agglomeration. Mejia-Dor­ antes (2012) applied the multinomial Logit model to compare the industry distribution before and after constructing new subway lines in the suburb of Madrid, Spain. For the coordination between transportation and urban agglomera­ tion spatial form, Mori (2011) thought there is a self-organizing rela­ tionship between urban economic system and transportation system. Picard (2010) and Arribas-Bel (2013) stated that transportation can promote the self-organizing of urban economy through analyzing the relationships between transportation cost and the numbers of enter­ prises, workers and consumers in a city. They concluded that with a certain transportation cost function, the urban scale would reach a stable level. Liu (2009) studied the interaction between urban agglom­ eration spatial form and transportation, and revealed the mechanism of a collaborative development. Other relevant studies and suggestions can be found in Lin (1999), Lv et al. (2010) and Xu et al. (2018a, b). In summary, most existing researches on the spatial form of urban agglomeration are based on empirical investigations with specific consideration, which cannot fully represent the general spatial form of urban agglomeration and provide systematic theoretical support. Thus, fully exploring the evolution power and evolution mechanism of urban agglomeration spatial form, is of great significance. Moreover, the

(a) Traffic demand matching models between transport modes under emergencies and disasters. (b) Models and algorithms of joint evacuation in multi-mode traffic networks under emergencies. (c) Models and algorithms of operation optimization and resource scheduling for rapidly recovering of multi-mode traffic system. (d) Collaborative management paradigm of air-ground integrated multi-level traffic network. 5. Sustainable development of urban agglomeration transportation system The development of urban agglomeration comprehensive trans­ portation system is expected to induce population migration, and pro­ mote industrial transformation and upgrading, thus leads to expansion 15

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Fig. 16. The roadmap for studying the evolution mechanism of urban agglomeration’s spatial form and transportation structure.

development of urban agglomeration multi-mode traffic network in China is expected to have an important influence on spatial structure. In order to improve the system’s planning, construction and operation, conducting systematic theoretical research on the mechanism between transportation and urban agglomeration spatial structure evolution is urgently required. The key scientific issues contained in this subtopic include:

industrial agglomeration effect by increasing returns to scale of pro­ duction and the industrial dispersion effect by resource mobility cost (Krugman, 1991). Since then, many scholars have extended the classical CP model, by incorporating industrial competition structure, regional economic diversity, micro enterprise characteristics, production effi­ ciency and other variables into the model. They have gradually devel­ oped the history and expectation model, the internal market effect model, the regional specialization model, the industrial diffusion model, etc., which provide explanations for the agglomeration and diffusion phenomena of industries in one or more cities, and reveal the in­ teractions between industrial agglomeration, population distribution, land use and trade flow (Fujita and Thisse, 2013; Brinkman, 2016). Urban economists, represented by Mills (1972) and others, put for­ ward the classical urban spatial structure model, which reveals the im­ pacts of transportation cost on location and spatial structure of urban residential zones. Transportation cost affects the spatial distribution of urban residents, thus the urban housing price, the value of land and the utilization pattern of land, and vice versa. Since then, scholars have expanded the classical urban spatial structure model. For example, Li et al. (2012a; 2015c), Li and Peng (2016) and Peng et al. (2017) respectively developed the optimization models that integrate subway and property, and the investment decision models that emphasize the influence of transportation infrastructure investment on urban spatial structure and urban economy. Ma and Lo (2013), and Ng and Lo (2015, 2017) further studied the spatiotemporal evolution of rail transit in­ vestment and housing construction. Geographic economics states that there is a bell-shaped relationship between transportation infrastructure improvement and industrial concentration. When transportation cost is high, manufacturers or firms diffuse their layouts in order to approach consumers easily; when transportation cost decreases, manufacturers gather to realize their increasing returns to scale; but when transportation cost further de­ creases, so that the transaction costs between manufacturers and

(a) Revealing the multiplicity and main driving factors of urban agglomeration spatial form evolution. (b) Identifying the topological structure bottleneck of urban agglomeration comprehensive traffic network. (c) Formulating the organizational mechanism for coordinated development of urban transportation and urban agglomeration spatial form. Fig. 16 shows the roadmap for studying the evolution mechanism of spatial form and transportation structure associated with urban agglomeration. 5.2. Coordinated optimization of comprehensive transportation and industrial development From the perspective of traditional geographical economics, the distribution of industries in a city is determined by exogenous factors such as geographical location, environment, policies and resource endowment of all subdivisions of this city. However, such exogenous factor determinism cannot explain why districts with similar geographical endowment may eventually develop into completely different space forms, some becoming city center and some reducing to the periphery. In order to explain this phenomenon, a so-called “coreperiphery (CP)” model has been developed, based on general equilib­ rium analysis framework. This model weighs two effects, i.e., the 16

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Fig. 17. The roadmap for studying the coordinated optimization of comprehensive transportation and industrial development.

(c) Developing the dynamic adjustment model of spatial structure and industrial layout in an urban agglomeration.

between manufacturers and consumers being extremely low, the con­ centration effect of manufacturers is no longer important, and firms diffuse again. This is verified by empirical studies from developed and developing countries (Graham, 2007; Yu et al., 2016). Anas and Xu (1999) proposed a generalized equilibrium model which considers urban industrial layout and population, showing that congestion pricing for road-sue would make urban space more compact. Wasmer and Zenou (2002) claimed that subsidies or taxes on urban transportation can effectively regulate the distribution of urban population and industries. Zenou (2011) analyzed the influences of three measures (improving the service level of public transport system, encouraging general investment and restricting migration) on urban employment, concluding that improving public transport has the greatest impact on urban economy. Knowles and Ferbrache (2016) evaluated the influence of light rail in­ vestment on urban economy, indicating that urban light rail investment is helpful to revitalize the central business district, promote urban employment and increase the price of real estate. We here have some comments on existing studies. First, these urban models usually take a single city as the research object and rarely consider the problems of urban agglomeration. It is of great significance to study the asymmetry and the morphological, structural characteris­ tics of urban agglomeration, together with the industrial layout and division of labor. Second, the comprehensive transportation system is a prominent feature of urban agglomeration, and different transport modes have specific actions to industry concentration. Now a network of intercity railways and highways in each China’s urban agglomeration are relatively developed. Third, this modern, multi-mode transportation network will inevitably promote the orderly diffusion of originally concentrated industries and labor, as a result, the urban space will be reconstructed. Therefore, exploring the dynamic evolution path of transport system and industrial layout, is meaningful to ensure the sustainable development of urban agglomeration. Then, the key scientific issues in this subtopic include:

The roadmap for studying these issues is shown in Fig. 17. 5.3. Sustainable development of land use and green transportation Relevant researches mainly include the influence of land use density on transportation system and traffic behavior. Northam (1979) sys­ tematically summarized the land use factors that affect the trans­ portation system, namely, the scale, density, design and layout of land use. Liu et al. (2010a) studied the impact of a city’s land use density on residents’ travel mode choice. Frank et al. (2008) applied the trip chain model and discrete choice model in analyzing the influences of urban land use and travel cost on transport modal split and trip chain. Litman (2005) presented 12 land use factors that affect urban transportation, including land use density, land use diversity and zone accessibility, etc. There are some researches studying the influence of transportation system on land use structure. Sclar and Schaeffer (1975) discussed the relationship between urban transportation system and urban spatial form, and addressed the impact of transportation on the evolution of urban spatial from. Mao et al. (2005) took Guangzhou as an example to systematically study the influence of rail transit on land use. Li (1998) pointed out that the accessibility of city zones will affect the land use structure of the city. Alonso (1964) putted forward the theory of urban land value, and verified the close relationship between urban trans­ portation system and land price, from the perspective of land location and rent. He (1998) calibrated the parameters used in a function describing the relationship between the rail transit travel cost and the land value along the rail line, using the data from Shanghai. Deng and Xu (2016) studied the relationship between passenger flow and land use around subway stations, by taking Beijing rail transit as an example. Some researches concern the coordination evaluation of land use and transportation. Fan (2010) established a comprehensive evaluation system for Beijing’s transportation, in which four aspects are considered: transportation service level, transportation resource allocation, coordi­ nation between transportation and its hard environment for develop­ ment, and adaptability between transportation and its soft environment for development. Yang (2007) developed a new index system to measure

(a) Revealing the interaction relationship between spatial structure and industrial layout in an urban agglomeration. (b) Developing the integrated optimization model of transportation infrastructure investment and industrial layout in an urban agglomeration. 17

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Fig. 18. The roadmap for studying sustainable development of land use and green transportation.

the win-win-win PPP contracts has the important theoretical and prac­ tical values in guiding the government to select projects and supervising investors to well construct and operate the projects. In the field of road transportation infrastructure construction, Tan et al. (2010) studied the non-dominant win-win Pareto effectiveness of built-operate-transfer (BOT) contract. They designed two strategies, i.e., the minimum traffic flow for pricing and the maximum value of toll, which would allow the government to obtain expected Pareto output while the investors to have full management freedom. Engel et al. (1997, 2001) studied how to formulate effective PPP contracts under the un­ certainty of traffic demand. They proposed the NPV auction mechanism to select road operators and designed the flexible contract theory. Tan and Yang (2012) applied the postponement strategy theory of operation management to develop the partially flexible contract mechanism. With this mechanism, the inert behaviors of private investors in cost control and risk aversion are discouraged. Shi et al. (2016b) investigated the contract design risks caused by the asymmetric cost information in tolled road projects. Watling et al. (2015) analyzed the cordon-based road-use pricing competition between two local governments which manage two connected traffic sub-networks. Chow and Regan (2009) applied the real option theory to study the design and investment optimization of general traffic network with uncertain travel demand. Li et al. (2015c) studied the investment timing of urban rail transit using also the real option theory. They found that the rail transit investment directly affects the urban spatial structure and the residents’ travel behavior. Li et al. (2013) investigated the layout and investment of radial subway network in a circular city. Their model considers the interests of multiple entities such as the government, in­ vestors and urban commuters, and derives the optimal layout and in­ vestment efficiency of urban residence and subway system. The study on airport investment and financing mainly focuses on dealing with the vertical and parallel market structures between public airport facility owners and oligopoly airlines. Zhang et al. (2010) and Wan et al. (2015) conducted comprehensive studies from the perspectives of airport congestion service pricing, airport capacity scale and distribution mechanism, passenger heterogeneity, and joint investment and financing between airports and airlines. In summary, urban agglomeration comprehensive transportation system has the characteristics of multi-mode integration, traffic behavior complexity and multi-scale organizing and operation collabo­ ration. As a result, the research on the system’s investment and financing

the coordination relationship between urban transportation and land use under the initiative of transit-oriented development. Using the data of 66 Chinese cities and the principal component method, Guo and Lu (2003) evaluated the development levels of urban transportation sys­ tems. Luo (2008) computed the coordination degree between trans­ portation and land use, using the data envelopment analysis method. Xu et al. (2016) proposed a model to coordinate the developments of transportation and land use on the basis of bi-level programming method. In a word, there exists an interactive feedback relationship between urban transportation and urban land use. Transportation changes the accessibility of city zones, which then directly causes the changes of urban land use pattern, intensity and form. Land use is certainly the root of developing transportation. The density, scale and layout of land use directly determine the amount and distribution of transportation de­ mand in the city, thus guiding the development of urban transportation. Therefore, it is of great significance to discuss the coordinated devel­ opment strategies of land use and green transportation in a sustainable urban agglomeration. The following key scientific issues should be covered in this subtopic. (a) Revealing the interaction mechanism of population distribution, land use distribution and comprehensive transportation network. (b) Constructing an evaluation index system for realizing the sus­ tainable development of land use and green transportation. (c) Developing an integrated coordination model of comprehensive transportation and land use for urban agglomeration. Fig. 18 shows the roadmap for studying the issues around the sus­ tainable development of land use and green transportation. 5.4. Investment and financing mode of transportation infrastructure Due to the shortage of public funds and the innovation and high efficiency of private funds, the public-private partnership (PPP) has become the most popular investment and financing mode in trans­ portation infrastructure construction. If travelers are willing to pay road tolls for saving time, investors can obtain satisfactory returns from operating the tolled roads, and social costs (including environment and land losses) can be sufficiently compensated. In this case, the PPP mode can realize the win-win-win target (Meng and Yang, 2002). Designing 18

H.-J. Huang et al.

Transport Policy xxx (xxxx) xxx

Fig. 19. The roadmap for studying the investment and financing mode of transportation infrastructure.

must extend and relax the assumptions made for single infrastructure project. One can systematically consider the investment timing and spatial layout of several projects, together with land use and industrial layout. Therefore, the key scientific issues contained in this subtopic include:

own national conditions and, meanwhile, draw on the experience of successful international development. A major project on scientific investigation on the comprehensive transportation system of urban ag­ glomerations in China has been approved by the National Natural Sci­ ence Foundation of China and is currently undergoing. Great efforts are required to realize the ideas of this project. We welcome any comments and suggestions from national and international counterparts.

(a) Developing the cost-benefit model of transportation infrastruc­ ture construction and operation. (b) Deepening the decision theory of infrastructure investment and financing for constructing a comprehensive transportation system. (c) Designing the contract mechanism of transportation infrastruc­ ture investment and financing.

Acknowledgements This work was supported by the National Natural Science Foundation of China (NSFC No. 71890971/71890970). We would like to thank all the participants of No. 179 Shuangqing Forum held in Beijing, May 2017, for their valuable comments and suggestions. Special thanks are given to Guangtao Wang, Qihu Qian, Jun Zhang, Zhihua Zhong, Hai Yang and Ziyou Gao for their keynote presentations at this forum con­ cerning urban agglomeration transportation. Contributions by Zuoyi Liu, Xiaoning Zhang, Zhe Liang, Jianjun Wu, Meng Xu, Zhichun Li, HK Lo, Renyong Guo and others who participated in the writing of the research proposals are appreciated. Any opinions and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the NSFC.

The roadmap for studying these issues is shown in Fig. 19. 6. Remarks China, with a population of nearly 1.4 billion, is undergoing the largest urbanization in history. This is expected to have a huge impact on population migration, productivity distribution and all other social and economic activities. Today, several huge urban agglomerations have sprung up. How to develop a comprehensive and modern transportation system which can effectively, smoothly and eco-friendly serve the intercity and intracity traffic is critical to the success of new urbaniza­ tion. With such an integrated and complicated system, we are required much more to design effective policies to coordinate intercity and intracity resources. Traditional policies, such as road tolling and traffic volume restriction, etc., face new challenges due to network complexity and the existence of multiple administrative regions in urban agglom­ erations. Emerging technologies provide new opportunities and resolu­ tions to mechanism design in an urban agglomeration. Mix policies may have vantage to solve trans-provincial problems. In this article, we have critically reviewed research progress and presented scientific ideas on transportation issues in the development of urban agglomerations in China. We believe that there are at least four topics worth further studying, i.e., travel behavior analysis and demand integration man­ agement within an urban agglomeration, design and operation optimi­ zation of the comprehensive transportation system, risk identification and emergency management of the system, and the sustainable devel­ opment issues of the system. To do so, China should take into account its

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