An Integrated and Personalized Traveler Information and Incentive Scheme for Energy Efficient Mobility Systems

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23rd International Symposium on Transportation and Traffic Theory, ISTTT 23, 24-26 July 2019, 23rd International Symposium on Transportation and Traffic Theory, ISTTT 23, 24-26 July 2019, Lausanne, Switzerland Lausanne, Switzerland

An Integrated and Personalized Traveler Information and Incentive An Integrated and Personalized Traveler Information and Incentive Scheme for Energy Efficient Mobility Systems Scheme for Energy Efficient Mobility Systems Chenfeng Xiongaa, Mehrdad Shahabibb, Jun Zhaoaa, Yafeng Yinbb, Chenfeng Xiong , Mehrdad Shahabi , Jun Zhao , Yafeng Yin , Xuesong Zhouc, Lei Zhanga,* Xuesong Zhouc, Lei Zhanga,*

Department of Civil and Environmental Engineering University of Maryland, College Park, MD 20742, USA DepartmentofofCivil Civiland andEnvironmental EnvironmentalEngineering EngineeringUniversity UniversityofofMaryland, Michigan,College Ann Arbor, Department Park,MI, MD48109, 20742,USA USA c b School of Sustainable and the Engineering Built Environment Arizona State University, Tempe, 85281, USA Department of CivilEngineering and Environmental University of Michigan, Ann Arbor, MI,AZ 48109, USA c School of Sustainable Engineering and the Built Environment Arizona State University, Tempe, AZ 85281, USA a

a b

Abstract Abstract Recently, the employment of different types of incentives in transportation systems to form advanced transportation congestion management Recently, thesolutions employment has garnered of different significant types ofattention. incentives Instead in transportation of using presumed systems or fixed-amount to form advanced incentives, transportation this paper congestion develops management has garnered significant attention. Instead ofscheme using presumed or fixed-amount incentives, this papertravel develops an integrated solutions and personalized traveler information and incentive to incentivize toward a more energy-efficient and mobility decisions. We have developed a behavior research and empirical modeling system to quantify the personalized travel monetary an integrated and personalized traveler information and incentive scheme to incentivize toward a more energy-efficient and mobility decisions. have developed behavior researchforand empirical modeling system toThis quantify the innovatively personalized integrates monetary incentives. Then, it We is integrated with a acontrol optimizer optimized incentive allocation. scheme incentives. it is integrated with a control optimizer for optimized incentive aallocation. This scheme innovatively integrates behavioral Then, modeling and optimization for travel incentive design. Through demonstrative case study for a large-scale behavioral modeling andtheoptimization incentive regions, design. the Through a demonstrative case studyisfor a large-scale transportation system in Washington for D.C.travel and Baltimore capability of the proposed scheme highlighted with transportation system in the Washington and Baltimore the capability the proposed schemeasiswell highlighted with significant system-level energy savings,D.C. reasonable insightsregions, on individual travelofbehavior responses, as superior significant system-level computational efficiency. energy savings, reasonable insights on individual travel behavior responses, as well as superior computational efficiency. © 2017 The Authors. Published by Elsevier B.V. © 2019 The Authors. Published by Elsevier B.V. Peer-review © 2017 The Authors. under responsibility Published by of Elsevier the scientific B.V. committee of the 23rd International Symposium on Transportation and Traffic Peer-review under responsibility of the scientific committee of the 23rd International Symposium on Transportation and Theory. Peer-review under responsibility of the scientific committee of the 23rd International Symposium on Transportation and Traffic Traffic Theory. Theory. Keywords: traveler information, incentives, monetary incentives, system model, control optimizer; Keywords: traveler information, incentives, monetary incentives, system model, control optimizer;

*

Corresponding author. Tel.: +1-301-405-2881; E-mail address:author. [email protected] Corresponding Tel.: +1-301-405-2881; E-mail address: [email protected] 2352-1465 © 2017 The Authors. Published by Elsevier B.V. Peer-review underThe responsibility of theby scientific of the 23rd International Symposium on Transportation and Traffic 2352-1465 © 2017 Authors. Published Elsevier committee B.V. *

Theory. Peer-review under responsibility of the scientific committee of the 23rd International Symposium on Transportation and Traffic Theory.

2352-1465  2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 23rd International Symposium on Transportation and Traffic Theory. 10.1016/j.trpro.2019.05.010

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1. Background In the realm of transportation, incentives have been adopted in a number of situations to promote certain behaviors and manage travel demand. For example, purchasing credits or tax refunds may apply in certain countries/states to nudge consumers towards adopting electrified vehicles and/or alternative-fuel vehicles (e.g., Diamond, 2009; Zhang et al., 2016). Some localities provide reserved parking or parking discounts if travelers regularly carpool to work/school (Jacobs et al., 1982; Brownstone and Golob, 1992; Wilson, 1992). More recently, monetary incentives have been explored and adopted to influence shorter-term travel behavior, such as daily modal shifts. For example, the Georgia Department of Transportation has implemented a program called “Gimme Five”, wherein a participant can earn $5/day if he/she decides to switch to a clean commute mode (i.e., car/vanpooling, transit, walk/bike, teleworking). The program lasts for a 30-day period, allowing participants to collect $150 of monetary incentives (Georgia DOT, 2015). A similar design is seen in the “CommuteSmart” program in Birmingham, Alabama, where $1/day is offered to motivate clean/smart-mobility alternatives (Regional Planning Commission of Greater Birmingham, 2016). While daily fees for certain time periods are seen in a few locations, another example of incentive is to offer a fixed, onetime payment if the participant joins a smart-mobility program (e.g. ridesharing) and remains active (meets a minimum requirement, such as the number of rides). Bay Area Regional Transit, for example, pays a one-time $50 gift card if an individual has registered and actually carpooled for a certain period of time. Admittedly, these incentive programs and schemes have obvious deficiencies. First, the incentives are mostly flatrate and not personalized or optimized. An advanced incentive mechanism should have the adaptation to person-level travel details, including one’s socio-economic attributes, travel destination, trip length, etc. Through personalization and an optimized design, the decision-maker may be able to achieve higher program objectives with the same program budget. Second, the incentive program should be hierarchical and incentivize a number of travel behavior dimensions, such as departure time choice, route choice, driving, and modal shifts. Compared to longer-term decisions, such as destination and car-ownership choices, these dimensions are relatively easy to influence. By changing these behavior dimensions for an individual traveler, the mitigation effect on urban congestion can be significant, due to its non-linear property (Daganzo, 1995). Finally, providing predictive travel information to individual travelers can be an effective incentivizing signal (Tseng, et al., 2013). For instance, when an alternative route can save an individual a significant amount of travel time, a monetary incentive may not be necessary to nudge them to change route. Existing infrastructure, such as the advanced traveler information system (ATIS), should be integrated in this regard. While it is rarely seen in the field that an incentive tool can address the abovementioned challenges, several seminal studies are worth mentioning. “Metropia” is a smartphone-based technology that employs an effective reward system to influence route and departure time shifts (Hu et al., 2015). By shifting to off-peak times and underutilized routes, users can earn a higher amount of rewards. This tool has been implemented in several cities in the U.S. as an incentivebased active demand management platform. Metropia adopts a factor-based reward assignment approach, while the allocation of the reward points is not personalized or optimized. Rey et al., (2016) also explored different ways of encouraging departure time shifts. They conducted experimental economics study on a lottery-based incentive scheme. The effectiveness of such incentives is found to be related to expectations of the outcome of shifting departure times and individual attitudes towards risks. Indeed, recent travel behavior research suggests that travel choices not only depend on trip characteristics, but are also highly segregated by individual attributes, such as age, employment status, and income (e.g., Prillwitz and Barr, 2011; Dixit, et al., 2017). Abovementioned incentive systems can certainly benefit from a well-designed behavioral research to better tailor and customize their incentive structure, and from a control optimizer to allocate the incentives more efficiently. Furthermore, personalized travel information can also serve as an effective “non-monetary” incentive, especially for commuting and time-sensitive travels (e.g., Kenyons and Lyons, 2003; Petrella and Lappin, 2004). Travel behavior shifts can be motivated by accurately estimated travel time and cost savings, based on real-time data analytics and predictive modeling (e.g., Mokhtarian, et al., 2015; Klein and Ben-Elia, 2016). If an individual can save a significant amount of time and cost by changing their behavior, and this information is deemed trustworthy, they are more likely to use the alternative even without any monetary rewards. Individuals are believed to be more responsive to personalized information than general messages to the public (Fujii and Taniguchi, 2006; Garling and Satoshi, 2009; Meloni, et al., 2014), such as information delivered through a dynamic message sign. Integrating the personalized

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information to the incentive structure seems to be a promising approach to effectively manage individual travel behavior. This is especially true when the budget constraint is a concern at the beginning of implementation. Motivated by these research needs, the objective of this paper is to develop an integrated and personalized travel information and incentive scheme that encompasses the following attributes: a) a system model (SM) consisting of integrated, person-level travel behavior, dynamic traffic, and energy use simulators; and b) a control optimizer (CO) based on behavior research, travel intent prediction, and incentive optimization innovations. We envision that this will enable public and private sector entities to guide travelers through personalized information and incentives to individually adjust their modes, departure time, and route choices, and to collectively minimize congestion and/or energy consumption in a multimodal transportation system. The remainder of the paper is organized as follows: Section 2 illustrates the overall framework of the developed scheme. Individual components of the scheme are elaborated in Section 3. In Section 4, the paper demonstrates the modeling system in a large-scale, real-world study area within the Washington, D.C.-Baltimore metropolitan region to showcase the capability of the developed scheme. Final conclusions and future work are offered at the end. 2. An Overview of the Integrated and Personalized Traveler Information and Incentive Scheme The integrated and personalized traveler information and incentive scheme aims at incentivizing the following categories of choices: • Modal shifts to transit and ride share: increase vehicle occupancy and reduce energy use. • Departure time choice: help users avoid congested periods on trips with flexible arrival time windows, and reduce peak-period demand and congestion for all travelers. • Pre-trip route choice: guide users to routes with less energy use before departure, and also reduce congestion and energy use for all travelers on routes already congested. • Driving style choices: incentivize users to practice eco-driving to reduce energy use. We propose a comprehensive loyalty program, implemented through smartphone apps, in-vehicle Bluetooth, and wireless networks, that employs long-term, pre-trip, and real-time incentives to influence choices. This incentive structure features simple rules for earning points, produces travel benefits to users, creates gaming-type activities and membership levels for long-term loyalty, balances monetary and non-monetary incentives, and utilizes social influence. A goal to minimize vehicle energy use with the developed scheme was set. Note that this goal can be revised according to different agency or project needs, such as minimizing the overall level of congestion or maximizing traffic throughputs. The framework has been developed towards this goal, as shown in Figure 1. There are four important modules in the framework, described briefly below and elaborated in the next sections: • System model (SM): an integrated agent-based behavior, traffic, and energy use simulation that can effectively simulate travel behavior, behavior responses to personalized incentives, and dynamics in traffic condition and fuel energy consumptions. It organically integrates an agent-based travel behavior model (AgBM), a dynamic traffic simulation engine (DTALite), and an efficient energy consumption estimator (MOVESLite). • Control optimizer (CO): an incentive optimizer that allocates incentives to each user based on SM-generated feasible alternatives, travel intent prediction, budget constraints, responses received from scheme users and nonusers. • Data and cloud infrastructure: A real-time transportation big-data infrastructure that provides access to available public and private sector data. The data infrastructure is constructed on cloud-based services for reliable and realtime accessibility. The infrastructure also archives personal data from subscribers and connects the SM-CO and app deployment through dedicated application programming interfaces (APIs). • Front-end user-interface (UI): A smartphone app UI that deploys the SM and CO and engages front-end users to influence their daily travel decisions.

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SM-CO APIs Control Optimizer

Fig. 1. The Framework of the Integrated Personalized, Real-time Traveler Information and Incentive Scheme.

The overall modeling framework for the SM is illustrated below. A successful SM must integrate a person-level travel behavior model and a vehicle-level traffic simulation model, which can interact on day-to-day and within-minute time scales. Our approach bases the SM development on proven products that have already been successfully integrated and used by transportation agencies, including: a) SILK agent-based travel behavior prediction, with or without personalized incentives (named for its emphasis on Search, Information, Learning, and Knowledge in personal travel decision making, Zhang, 2007; Xiong, 2015); b) DTALite dynamic traffic assignment (DTA) model for traffic simulation (named for being a computationally “Lite” DTA, Zhou and Taylor, 2014); and c) MOVESLite energy estimator, a fast-computing adaptation of the industry-standard EPA MOVES model (Frey and Liu, 2013; Zhou et al., 2015) that accurately simulate energy and emissions based on second-by-second vehicle trajectories produced by DTA. The SILK model incorporates multidimensional travel choices including travel mode, departure time, pre-trip routing, and en-route diversion choices. All travel choices are modeled on empirically-observed behavior dynamics data without assumptions of perfect information or substantial rationality. Behavior formation and adjustments are estimated with rule-based methods that are computationally more efficient. Each individual traveler forms and adjusts their behavior based on learned travel conditions, subjective beliefs and expectations, information, and system control (including incentives such as rationing and credit-based pricing, Lawphongpanich and Yin 2010; Zhu et al., 2013). Another merit of SILK is the replacement of typical user equilibrium process using the stopping criteria of agent-based behavioral adjustments. Theorized using agents’ satisficing behavior and imperfect knowledge, the SILK modeling process will lead to a steady state that every traveler in the system will stop seeking behavioral changes. And the transportation system is guaranteed to reach such a steady state with a much faster converging process (Xiong, Zhou, and Zhang, 2017). This greatly reduces the number of iterations required for simulation. SILK then passes a list of travelers, as well as their trip purposes, departure times, origins, and destinations, to DTALite. DTALite takes the routing information from the behavior model and simulates traffic dynamics, while tracking individuals’ trajectories in the multimodal transportation network. DTALite has successfully applied Intel’s multicore parallel computing technology, OpenMP. Two studies—one on the D.C.-Baltimore network and a recent DTA deployment effort with North Carolina DOT—found that the average DTALite simulation speed is two to six times faster than other similar software packages. Overall, the SILK, DTALite, and MOVESLite together bring down the simulation run time significantly, making it feasible to develop a large-scale multimodal simulation of the transportation systems and its energy consumptions. This provides the system-modeling foundation for evaluating the effectiveness of any existing or proposed incentive structure. Nevertheless, how travelers behaviorally respond to incentives under different contexts, how such behavioral responses can be used to quantify the amount of incentives needed, and how the incentive allocation across travelers

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can be optimized to achieve system level energy goals remain largely unexplored. The remainder of the paper will focus on the technical approaches addressing these research needs. SILK Travel Behavior Model

Energy Estimator Agent i

Behavior prediction without incentives

5-sec time scale

Behavior response prediction No

change behavior?

MOVESLite Vehicle Details in with Section 2.1.2 Occupancy Estimator

Personalized incentives

DTALite Dynamic Network Simulator

Yes Travel mode

Departure time

Pre-trip route

En-route diversion

Driving style

Current and predicted network conditions, vehicle trajectories Agent List

Update agent i

5-sec time scale Habitual behavior

1-sec time scale

Agent 1 Agent 2 Agent i

Agent n

No no active agents?

Yes

BUE

Fig. 2. The system model (SM) modeling framework.

3. Travel Behavioral Model for Personal Travel Incentives One of the purposes of the SM is to provide an accurate and relatively fast simulation of the transportation systems for effective travel incentive design. The individual-level incentive mechanism is modeled and quantified by a principal-agent theoretical model. Then an overarching control optimizer allocates the incentives to agents. This paper focuses on the monetary incentives and non-monetary information. Findings indicate that information and incentives have a more significant impact, compared to gamifications and social comparison. Another working paper analyzes the potential of behavioral shifts resulted from forming social comparison and social norms (Velez et al., 2017). 3.1. The Behavioral Model The principal (i.e., system controller) has an overall goal, such as system-level energy savings, congestion mitigation, and so forth. Without loss of generality, we assume that the principal maximizes a program gain (e.g., the total energy savings) as the goal in the theoretical model. To achieve the goal, the principal offers monetary incentives in order to motivate all its agents (users) to make certain travel decisions individually, such as carpooling, using public transit, or switching to off-peak departure times. At this stage, budget constraint is not considered, as the principal will try its best to nudge each agent towards the most energy efficient alternatives. When conducting the system-level control and incentive allocation, the budget constraint will be incorporated in the control optimizer, wherein all agents are marshaled and coordinated to achieve system optimum. In other words, the problem is decomposed into a twostage design: • Stage 1: For each travel behavior that might be specified (incentivized), identify the best compensation scheme to offer the agent. • Stage 2: For each agent, determine the best choice of behavior to incentivize via a system-level optimization approach based on Stage 1 incentives. The agent has their habitual travel behavior, which sets up a reservation payoff level as a benchmark for their decision-making under incentives. For instance, the payoff level for driving alone becomes the agent’s reservation payoff level, if their habitual travel mode is driving alone. The agent is assumed to be an expected utility maximizer,

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who will arrange their travel, take the monetary incentive p into account, and decide if they want to switch to the incentivized travel alternative or not. Their reservation constraint is assumed to be: 𝑣𝑣(𝑝𝑝𝜏𝜏 ) − 𝑔𝑔𝜏𝜏 ≥ 𝑣𝑣̅

(1)

where 𝑣𝑣̅ denotes the reservation payoff level. 𝑣𝑣(𝑝𝑝𝜏𝜏 ) denotes the payoff from receiving incentive prize 𝑝𝑝.  denotes their decision under the incentivization. 𝜏𝜏 ∈ {𝜏𝜏1 , 𝜏𝜏2 , … , 𝜏𝜏𝑗𝑗 , … , 𝜏𝜏𝑛𝑛 } . The set includes different alternative routes, departure times, and modes. 𝑔𝑔𝜏𝜏 represents the additional payoff loss for following the incentivization to choose . For instance, it represents the excessive travel time associated with switching to a public transit alternative. On the other hand, 𝑔𝑔𝜏𝜏 can be negative in some other cases where following the incentivization will actually improve the travel level of service, such as switching to less congested travel routes or departure times. The identification of alternatives with improved traffic condition serves as a type of non-monetary incentive that also motivates the adoption of . With lower 𝑔𝑔𝜏𝜏 , a weakly risk-averse agent will need a smaller amount of p to meet the reservation constraint, Eq (1). It is assumed that agents’ behavioral responses  are completely observable. Through the spatial-temporal tracking implemented in the front-end smartphone app, the space-time trajectories of the users are fully observed and can be used to infer their routes, departure times, and modes. Through artificial intelligence techniques, the authors have found the accuracy of successful inference (above 95% accuracy for detecting auto, metro, and slow-mode travels, above 90% for bus travels, Xiong, et al., 2017). This assumption is important for the principal-agent model to work properly, as the allocation of incentives 𝑝𝑝(𝜋𝜋) is directly related to the program gain, which is stochastically dependent on the agent behavior described by 𝑓𝑓(𝜋𝜋|𝜏𝜏). In control of each agent, the principal’s goal is thus formulated as an optimal contracting problem. It maximizes the program gain 𝜋𝜋 (e.g., the equivalent dollar value of the total energy savings) less the allocated monetary incentives for this agent, subject to the agent’s reservation constraint. Note that the principal still needs to make a decision on which agent(s) to incentivize based on his budget (described in Section 3.3). 𝑚𝑚𝑚𝑚𝑚𝑚 ∫(𝜋𝜋 − 𝑝𝑝(𝜋𝜋))𝑓𝑓(𝜋𝜋|𝜏𝜏)𝑑𝑑𝑑𝑑 𝜏𝜏

ሺʹሻ

𝑠𝑠. 𝑡𝑡. ∫ 𝑣𝑣(𝑝𝑝)𝑓𝑓(𝜋𝜋|𝜏𝜏)𝑑𝑑𝑑𝑑 − 𝑔𝑔𝜏𝜏 ≥ 𝑣𝑣̅

ሺ͵ሻ

min ∫ 𝑝𝑝(𝜋𝜋)𝑓𝑓(𝜋𝜋|𝜏𝜏)d𝜋𝜋

(4)

−𝑓𝑓(𝜋𝜋|𝜏𝜏) + 𝜆𝜆𝑣𝑣 ′ (𝑝𝑝)𝑓𝑓(𝜋𝜋|𝜏𝜏) = 0

(5)

𝑝𝑝∗ = 𝑣𝑣 −1 (𝑣𝑣̅ + 𝑔𝑔𝜏𝜏 )

(6)

Given that the incentive structure will specify the alternative , choosing p() for the maximization of Eq. (2) is equivalent to solving a minimization of the expected value of monetary incentives. The objective function (Eq. 2) is rewritten as: 𝑝𝑝

Denoting the multiplier of the reservation constraint (Eq. 3) using 𝜆𝜆, the incentive p should satisfy the Kuhn-Tucker first-order condition: Thus, to incentivize travel alternative , the principal should offer an incentive p*, such that the agent receives exactly their reservation payoff level. Otherwise, the principal can always lower the amount of incentive while still getting the agent to accept it. Eq. (6) is then empirically tested using the collected sample data.

3.2. Empirical Data Collection

This model is empirically quantified by an incentive and behavior response survey, with 1,022 representative samples collected in the D.C. and Baltimore metropolitan areas in 2016~2017. The spatial distribution of the samples by their residential location is illustrated in Fig. 3(a). Then we have performed a resampling and weighting procedure

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to correct potential sampling bias based on the U.S. Census. Each subject was first asked to report their typical daily travel itinerary. Based on the reported trip, three sets of stated preference games were generated. In each scenario, monetary incentives were provided in one or several alternatives. In the first set of SP games, route and departure time alternatives are included as options in comparison to the subjects’ original travel choice. In the second and third games, different modal options are offered, including ride-sharing, car-access transit, walk/bike-access transit, etc. Figure 3(b) illustrates the observed responses, visualizing the percentage of subjects choosing to stick to their original travel plan or to change route, departure time, or travel modes.

a.

b.

Fig. 3. (a) Spatial Distribution by Residential Location of the Survey Samples; (b) Behavioral Responses in the Stated Preference Survey.

Travelers are reluctant to change their habitual travel plans. Under different SP scenarios, a significant amount of survey subjects stated that they will stick to their original choices, even if incentives were offered for making behavioral changes. As expected, this observed behavioral inertia was more significant when subjects were in the consideration of travel mode choice. On the other hand, we also found that in some scenarios, subjects did not need incentives at all. Figure 4 illustrates the percentage of subjects who had chosen to change their original travel plans with or without any monetary incentives. About 12% of subjects were willing to adjust their departure time and/or routes even if no monetary incentives were offered. This indicates that, other than monetary incentives, the traffic condition changes also play a significant role in influencing travel behavior. A successful intervention should inform the users about their travel options in a way that information on travel time/cost savings can also be used to influence their behavior. 100% 50%

87.3%

0%

12.7% Change Routes (incentive = 0)

72.8% 27.2% Change Routes (incentive > 0) change

87.8%

80.1%

12.2%

19.9%

Change Departure Time Change Departure Time (incentive = 0) (incentive > 0) not change

Fig. 4. Percentage of behavioral change under different incentives.

3.3. Empirical Modeling Results The following alternative-specific payoff functions are specified and estimated for each agent, in order to quantify the monetary incentive term p*.

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(7)

𝜈𝜈 = 𝛃𝛃 ⋅ 𝐗𝐗 + 𝛄𝛄 ⋅ 𝐙𝐙 + 𝜃𝜃 ⋅ 𝑝𝑝 + 𝜖𝜖

where X denotes a vector of alternative-specific variables, including travel times, travel costs, and schedule delays. Z denotes a vector of socio-economic characteristics for each agent, including gender, age, education level, income level, and trip purpose. p denotes the monetary incentive. Note that the reservation payoff function 𝑣𝑣̅ uses the same specification, except that incentive p is not included. We have built two models: a route/departure time behavior response model and a travel mode choice response model. The payoff function parameters for v and 𝑣𝑣̅ are estimated through a maximum likelihood estimation approach. The estimated Eq. (7) provides the foundation for quantifying the incentives. The estimated results are reported in Table 1 and Table 2. Table 1. Modeling Results of the Route and Departure Time Behavior Response under Monetary Incentives Variables

Estimate

Std.

t-value

Original travel plan: constant -fixedRoutes: constant -0.462 0.112 -4.125 Departure times: constant -0.471 0.114 -4.138 Travel time (min.) -0.086 0.005 -18.601 Total costs ($) -0.131 0.015 -8.911 Schedule delay early (min.) -0.008 0.001 -5.116 Schedule delay late (min.) -0.010 0.002 -5.520 Monetary incentive ($) 0.700 0.044 15.793 Routes: gender = male -0.137 0.056 -2.435 Departure times: gender = male -0.305 0.058 -5.244 Routes: higher income -0.170 0.029 -5.865 Departure times: higher income -0.108 0.030 -3.649 Routes: commute trips -0.029 0.056 -0.518 Departure times: trip_purpose -0.452 0.058 -7.855 Routes: age -0.099 0.020 -5.035 Departure times: age -0.219 0.020 -10.852 Routes: education 0.093 0.021 4.508 Departure times: education 0.170 0.021 7.973 Log-Likelihood: -12861 McFadden R^2: 0.044 Likelihood ratio test: chi-sq = 1196.3 (p.value = < 2.22e-16) a Significance Code: 99% ***; 95% **; 90% *.

Pr(>|t|)

Sig.a

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.015 0.000 0.000 0.000 0.605 0.000 0.000 0.000 0.000 0.000

*** *** *** *** *** *** *** ** *** *** *** *** *** *** *** ***

Table 2. Modeling Results of the Mode Choice Behavior Response for Longer-Distance Travels Variables

Estimate

Std.

t-value

Pr(>|t|)

Sig. a

Original travel plan: constant Ride-sharing: constant Car-access transit: constant Walk/bike-access transit: constant Travel time (min.) Total costs ($) Monetary incentive ($) Ride-sharing: age Car-access transit: age Walk/bike-access transit: age Ride-sharing: gender = male Car-access transit: gender = male Walk/bike-access transit: gender = male Ride-sharing: higher income Car-access transit: higher income Walk/bike-access transit: higher income Ride-sharing: commute Car-access transit: commute Walk/bike-access transit: commute Log-Likelihood: -7,726 McFadden R^2: 0.065 Likelihood ratio test: chi-sq = 1080.2 (p.value = < 2.22e-16) a Significance Code:

-fixed0.206 -0.440 -0.567 -0.015 -0.050 0.069 -0.331 -0.264 -0.564 0.399 0.176 0.676 -0.118 -0.084 0.197 -0.253 -0.405 -0.534

0.114 0.137 0.155 0.002 0.003 0.043 0.023 0.028 0.033 0.066 0.081 0.093 0.033 0.040 0.045 0.067 0.080 0.093

1.815 -3.208 -3.652 -8.197 -18.772 1.599 -14.170 -9.517 -16.925 6.018 2.170 7.242 -3.551 -2.116 4.378 -3.783 -5.067 -5.764

0.070 0.001 0.000 0.000 0.000 0.110 0.000 0.000 0.000 0.000 0.030 0.000 0.000 0.034 0.000 0.000 0.000 0.000

* *** *** *** ***

99% ***; 95% **; 90% *.

*** *** *** *** ** *** *** ** *** *** *** ***

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As shown in Table 1, the negative constants reveal that travelers tend to not change their original travel decisions, which can be explained by habitual behavior or behavioral inertia. Coefficients for travel time, costs, and schedule delay variables are negative to the payoff. An increase of these variables in alternatives will discourage the agents in choosing these alternatives. On the other hand, the monetary incentives play a positive role in influencing a subjects’ utility (i.e., the higher the incentive, the more likely subjects will choose the associated travel option). The influence from socioeconomic characteristics of the travelers has also been identified in the model. For instance, female travelers and travelers with higher educational background are more likely to consider and change travel choices, while commuters and older citizens are more willing to stick to their original travel plan. Table 2 presents the estimated coefficients for the mode choice behavior responses under monetary incentives. More significant inertia in switching to different transit options is found for car-access transit and walk/bike-access transit. In contrast, travelers are more willing to embrace ridesharing as a major modal shift response under monetary incentives. The coefficient of incentive is positive, but relatively smaller compared to its coefficient in the route and departure time model. This is as expected, since it is usually more difficult to incentivize modal shifts. Similar behavioral response patterns are identified for different population groups to what we have described for the route/departure time modeling results, with the exception of higher-income groups, who are more willing to switch travel modes to walk/bike access transit. This counter-intuitive finding may be related to unobserved factors, such as their occupations and prosocial attitudes towards environment and energy saving. Based on Equations (6) and (7) and the estimated parameters, we are able to quantify the amount of incentives needed for different travel options. To incentivize a commuter who typically would drive to work to instead use public transit, the monetary incentive p should make the payoff level of using public transit at least as good as their payoff for driving, compensating the likely disutility from the excessive travel time (in X) and individual preferences based on personal characteristics (in Z). In other cases, wherein the provided alternatives can save a large amount of travel time and/or travel costs (e.g., an alternative route when the original route is too congested due to unexpected events), it is often observed that subjects often do not need much, if any, monetary incentive to switch to those alternatives. Under this circumstance, the predicted traveler’s information of travel time savings, cost savings, and fuel savings will be equivalent to the monetary incentives in motivating behavioral changes. As an illustrative example, let us consider a male traveler with younger age and in the low-income group. Let us assume his typical commute trip is 30-minute driving with 2$ of fuel cost and 8$ of parking cost. If there exists a lightrail option for his commute that takes 40-minute travel time and 1$ transit fare, it will need 6.8$ of monetary incentives to make this traveler willing to start using the transit based on the estimated model. This incentive can be further personalized according to individual travel conditions. For example, the recurrent 30-minute commute may be deteriorated to 50 minutes in severe weather condition or by a serious accident on the associated route. If that is the case, a 2.4$ incentive combined with the accurate traveler information will suffice to make the traveler willing to use transit. Also, the empirical study has found that it is relatively easier to influence non-commute trips. For a same setup of travel conditions, a 0.9$ incentive can effectively motivate the example traveler to change travel modes for noncommute travels. This empirical study enables researchers to determine the personalized monetary incentives that are needed for nudging certain travel behavioral shifts, which can be implemented in different advanced travel demand management and modeling practices. This quantification is crucial for improved effectiveness of any existing/proposed travel incentive programs. In our integrated modeling system, this is directly linked to a Control Optimizer that optimizes the incentive allocation. Details are offered in the next section. 4. The Control Optimizer (CO) of Individualized Incentives An efficient design for allocation of incentives can induce the desired travel behavior among the application users and achieve a system-wide performance standard. This section sets forth a plan for designing a framework which enables the allocation of incentives to the platform users. The goal of the incentive allocation problem is to target and pinpoint limited number of users and incentivize them to a different travel alternative in order to achieve to a desired level of energy savings across the entire network. The Control Optimizer (CO) is formulated as a mathematical program and

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Fig. 5. The control optimizer (CO) framework. ′

Toward this end, assume 𝐼𝐼 and 𝐼𝐼 denote the set of likely users and non-users respectively. In designing the control problem, we consider that the vector of monetary incentives P=[𝑝𝑝11 , … 𝑝𝑝1𝑗𝑗 , … , 𝑝𝑝𝑖𝑖𝑖𝑖 ] is given for every user i ∈ 𝐼𝐼. In this vector, 𝑝𝑝𝑖𝑖𝑗𝑗 denotes the optimal incentive determined in the principal-agent model(discussed in Chapter 3) for each agent i to switch to travel alternative j ∈ 𝑖𝑖 .Where 𝑖𝑖 is the set of feasible alternatives for user i. In addition, vector Y = [𝑦𝑦𝑖𝑖1 , … , 𝑦𝑦𝑖𝑖𝑖𝑖 ] represent the binary decision space in which 𝑦𝑦𝑖𝑖𝑖𝑖 = 1 indicates that agent i would receive incentives on alternative j and 0 otherwise. Finally, consider E=[𝑒𝑒1 , … 𝑒𝑒𝑖𝑖 ] as the vector of energy consumption for every traveler i ∈ 𝐼𝐼⋃𝐼𝐼′ . This vector of outputs can be examined analytically or computed via a simulation-based approach. In this framework, the SM simulation engine is used to derive energy estimations. It is worth nothing, that the energy consumption estimation, as the output of the simulation depends on the decisions vector Y. The incentive assignment problem can be formulated as the following optimization problem (P1): Model P1: min ∑𝑖𝑖 ∈𝐼𝐼⋃𝐼𝐼′ 𝑒𝑒𝑖𝑖 (Y)

s. t. ∑𝑖𝑖 ∈𝐼𝐼 ∑𝑗𝑗 ∈𝑖𝑖 𝑝𝑝𝑖𝑖𝑖𝑖 𝑦𝑦𝑖𝑖𝑖𝑖 ≤ 𝐵𝐵 ∑𝑗𝑗 ∈𝑖𝑖 𝑦𝑦𝑖𝑖𝑖𝑖 ≤ 1

∀𝑖𝑖

(8) (9) (10)

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𝑦𝑦𝑖𝑖𝑖𝑖 = 0 𝑜𝑜𝑜𝑜 1 ∀𝑖𝑖, 𝑗𝑗 ∈ 𝑖𝑖

11

(11)

In the above formulation, 𝑦𝑦𝑖𝑖𝑖𝑖 is the binary decision variable that is equal to 1 if user i is selected. B denotes the total budget of incentives. The goal of the objective function (8) is set to minimize the total energy consumption in presence of the constraints on total amount of incentives. The objective function can be adapted for other program or agency goals, such as maximizing travel time savings or reducing corridor congestion. Equation (10) guarantees that each traveler is assigned to the maximum of one travel option. The above problem belongs to the family of simulation optimization problems as the energy consumption 𝐸𝐸 has been generated by an exogenous simulation model. This relation is defined as: E = U(Y), where U represents the simulation engine. The fact that there are no available mathematical properties such as gradient or hessian for the simulator U, handling problem P1 for large-scale problem which involves a large number of users and travelers is complicated. In addition, the traditional simulationoptimization methods are unable to solve this model as the space of the binary variables is huge. Running multiple rounds of simulation to evaluate the solution performance is practically out of question due to the huge computational burden. In order to solve Model P1, we considered two different scenarios based on the presumed interaction between the technology users and non-users and customized two heuristics methods which are able to deliver a good quality solution in a reasonable amount of time. 4.1. Scenario I: Low penetration rate When the technology penetration rate is relatively low, the influence of the incentive control on the non-users is relatively small. We first consider a simplified scenario where such influence is assumed away. This assumption would lead to the case where every traveler (application user or non-user) is indifferent to the choices made by other travelers. More specifically, the decisions made by each platform user is treated as independent of each other, and assumes that the change of behavior by platform users would not affect the decision made by non-users and vice versa. Given that the user accepts the incentives and follow the platform direction, a reduction in total energy consumption would be experienced. Therefore, under such an assumption, there is no need to re-simulate and evaluate the impact incentive allocation on the state of the transportation network. Thus, by defining 𝑒𝑒𝑒𝑒𝑖𝑖𝑖𝑖 as the amount of energy which can be saved if user i switches to alternative 𝑗𝑗 ∈ 𝑖𝑖 , model P1 can be reduced to the following Binary Linear Program (BLP): Model P2:

max ∑𝑖𝑖 ∈𝐼𝐼 ∑𝑗𝑗 ∈𝑖𝑖 𝑒𝑒𝑒𝑒𝑖𝑖𝑖𝑖 𝑦𝑦𝑖𝑖𝑖𝑖

(12)

∑𝑗𝑗 ∈𝑖𝑖 𝑦𝑦𝑖𝑖𝑖𝑖 ≤ 1

(14)

∑𝑖𝑖 ∈𝐼𝐼 ∑𝑗𝑗 ∈𝑖𝑖 𝑝𝑝𝑖𝑖𝑖𝑖 𝑦𝑦𝑖𝑖𝑖𝑖 ≤ 𝐵𝐵

(13)

𝑦𝑦𝑖𝑖𝑖𝑖 = 0 𝑜𝑜𝑜𝑜 1 ∀𝑖𝑖, 𝑗𝑗 ∈ 𝑖𝑖

(15)

∀𝑖𝑖

In the above formulation equation (12) is the objective function which seeks to minimize the total energy reduction as a result of incentivizing user i toward alternative 𝑗𝑗 ∈ 𝑖𝑖 . Equations (13)-(15) define the feasible space of the solution. Model P2 belongs to the family of Knapsack problems which are proved to be NP hard and have been extensively studied in the literature. However, due to the large size of the problems, the applications of classical algorithms, such as dynamic programming techniques, are limited to solving this problem. We should also point out that the feasible spaces related to formulations P1 and P2 are exactly the same (i.e., the binary space of the agents along with their alternatives, Eq. 9-11). The distinguishing term of the two formulations is the objective function, the energy consumption related to each agent. By assuming low penetration rate for the platform users, the energy consumption can be evaluated using only one round of simulation. This is mostly attributed to the fact that users and non-users change of behavior is considered to be independent of each other. However, such independence assumption

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is no longer valid in the case of high penetration rate, as users’ change of behavior as shown by the results would impact the decisions made by other travelers. In order to cope with the large-size decision variables in formulation P2, we developed a greedy heuristic method based on sorting and selection of the most efficient users which provide a good quality solution in a matter of seconds. The main advantage of the greedy search heuristic is its low level of complexity of O(nlogn), which is determined by the complexity of sorting procedure. This is particularly attractive in our case as the number of the variables is large. The greedy algorithm does not guarantee optimality. However, its average performance is competitive, as proved by Diubin and Korbut (2003, 2008) and Mastin and Jaillet (2015). In particular, assuming the budget B= λn, where n is the total number of users, the algorithm has the asymptotical tolerance t≥0 (i.e. the difference to the true optimal solution) if λ exceeds the value of 1/2-t/3. In other words, a better solution can be achieved by increasing λ. In the numerical study of this paper, we have the daily budget of 3,000,000 points which are allocated to 20% of the population (1,600,000 users). Therefore, λ in our case is equal to 1.87, which is greater than the threshold. Thus, the asymptotic tolerance is zero. The details of the sorting and selection algorithm have been depicted in Figure 6. A ranking and selection algorithm for incentive allocation problem Initialization: Maximum amount of points is B a) Generate the energy reduction vector: E=[𝑒𝑒10 , … , 𝑒𝑒𝑖𝑖0 ] b) Get the point vector: P=[𝑝𝑝11 , … , 𝑝𝑝𝑖𝑖𝑖𝑖 ]]

Complete one round of simulation

Generating Travel Alternatives 1. 2. 3.

4.

For ∀𝑖𝑖 ∈ 𝐼𝐼: Identify a set of travel alternative 𝑗𝑗 ∈ 𝑖𝑖 For ∀𝑗𝑗 ∈ 𝑖𝑖 : i. Get energy consumption 𝑒𝑒𝑖𝑖𝑖𝑖 ii. Calculate the energy consumption reduction as: 𝑒𝑒𝑒𝑒𝑖𝑖𝑖𝑖 = 𝑒𝑒𝑖𝑖0 − 𝑒𝑒𝑖𝑖𝑖𝑖 iii. Find the efficiency index 𝑒𝑒𝑒𝑒𝑖𝑖𝑖𝑖 as: 𝑒𝑒𝑖𝑖𝑖𝑖 𝑒𝑒𝑒𝑒𝑖𝑖𝑖𝑖 = 𝑝𝑝𝑖𝑖𝑖𝑖  ‹†–Š‡ƒŽ–‡”ƒ–‹˜‡𝑗𝑗 ∗ ™‹–Š–Š‡Š‹‰Š‡•–𝑒𝑒𝑒𝑒𝑖𝑖𝑖𝑖∗  ∗ ‡–—”˜‡…–‘”EF = [𝑒𝑒𝑒𝑒1𝑗𝑗 , … , 𝑒𝑒𝑒𝑒𝑖𝑖𝑖𝑖∗ ]

User’s Ranking and Selection

‘”–˜‡…–‘”EF ƒ……‘”†‹‰‡ˆˆ‹…‹‡…›‹†‡š𝑒𝑒𝑒𝑒𝑖𝑖𝑖𝑖∗ ƒ†”‡–—”𝐸𝐸𝐸𝐸 ∗  5. ͸Ǥ ͹Ǥ ͺǤ ͻǤ

For ∀ 𝑒𝑒𝑒𝑒𝑖𝑖𝑖𝑖∗ ∈ 𝐸𝐸𝐸𝐸 ∗ : ‹ˆB ≥ Ͳ–Š‡ •‡– 𝑦𝑦𝑖𝑖𝑗𝑗 ∗ = 1

††𝑒𝑒𝑖𝑖𝑗𝑗 ∗ –‘˜‡…–‘”𝐸𝐸 ∗  Ž‡–𝐵𝐵 = 𝐵𝐵 − 𝑝𝑝𝑖𝑖𝑗𝑗 ∗ 𝑦𝑦𝑖𝑖𝑗𝑗 ∗ 

10. Return 𝐸𝐸 ∗ and 𝑌𝑌 ∗

Fig. 6. Sorting and Selection Algorithm.

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The goal of this proposed heuristic algorithm is to select the best alternatives from a limited finite number of choices in order to maximize energy consumption reduction. The algorithm starts by finishing one round of simulation to generate the energy reduction vector 𝐄𝐄 and point vector 𝐏𝐏. Given the results of the simulation the next step is to generate and create a viable set of alternatives 𝑖𝑖 for every user 𝑖𝑖 ∈ 𝐼𝐼. Assuming 𝑒𝑒𝑖𝑖0 represents user’s base energy consumption, and defining 𝑒𝑒𝑖𝑖𝑖𝑖 as the energy consumption for every alternative 𝑗𝑗 ∈ 𝑖𝑖 , energy reduction is defined as 𝑒𝑒𝑒𝑒𝑖𝑖𝑖𝑖 = 𝑒𝑒𝑖𝑖0 − 𝑒𝑒𝑖𝑖𝑖𝑖 . Once the energy reduction vector is generated, we are able to calculate an efficiency index (𝑒𝑒𝑒𝑒𝑖𝑖𝑖𝑖 ) for each travel alternative 𝑗𝑗 ∈ 𝑖𝑖 . The efficiency index, which is the ratio of energy reduction over points for every alternative, reflects how many units of energy per amount of incentive can be saved. Higher margins of the efficiency index would indicate more potential for energy savings for the given incentives. Once the efficiency index has been calculated for every travel alternative, a two-stage ranking is applied to sort the users according to their travel alternatives. The alternatives for every user are first ranked, and the list of users is identified with their best alternatives. Subsequently, all the users can be ranked according to their best alternatives. Once the sorted list of users is on hand, the points are allocated to the ranked lists of users until the entire budget is exhausted. The overall framework of the control problem in the case of low penetration rate is illustrated in Figure 5. The first step is the agent-based simulator, which provides the system and person-level behavior of travelers. In particular, the network simulator provides the decisions regarding the departure time, route, and mode choice of every traveler. Given that the agents’ trips in the entire study area are simulated, a set of viable travel alternatives consisting of a combination of different travel modes and departure time for each user can be generated. The next step is to calculate the corresponding number of points and energy consumption associated with each of the travel alternatives within the choice set. Toward this end, by using the outputs of the agent-based simulator, the behavior component provides the number of points associated with every alternative in the choice set. Similarly, the energy for each traveler is estimated and predicted, considering different travel alternatives are taking place according to the energy estimator component. Finally, given the outputs from the behavior and energy estimator components, the role of the incentive design unit is to determine the minimum number of points offered to the users, such that the criterion on energy reduction is satisfied. The assigned incentives and the decisions made by the travelers are then fed back to the agent-based traffic simulator and behavior model to be considered for the next day’s predictions of users’ behavior. 4.2. Scenario II: High Penetration Rate In this section, we consider solving the incentive allocation problem in presence of high penetration rate in the market. In particular, we extend the framework on two fronts; capturing the non-users behavioral response and also re-evaluating users change of behavior as a result of receiving incentives. More specifically, in the first scenario, we assume that every user’s change of behavior does not have any impact on other users and nonusers travel behavior. In the current part, such assumption has been relaxed and a new heuristic is proposed in order to account for the interaction between agents of different types. In developing this algorithm, we assume that we have the computational budget of K simulation runs in order to conduct the simulations and re-evaluate the impact of the incentive allocation plans. The details of the search heuristic algorithm for solving the incentive allocation problem in presence of high penetration rate for the incentive scheme has been presented in Fig. 7. Leveraging from sorting and ranking heuristic proposed in the previous section, the search heuristic simply compares users’ performance at two successive iteration of k and k-1. If users’ energy consumption after moving from iteration k to iteration k+1 is improved we consider incentivizing her for the rest of the simulation period and will adjust the budget accordingly. We proceed with the next round of simulation given the new allocation assignments and get the new-sorted list of performance measures. Finally, the algorithm stops when either the budget is exhausted or the maximum number of simulation rounds is reached.

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A search heuristic for incentive allocation problem Initialization: Maximum amount of points is B Set the iteration counter k = 1, Set the maximum number of iterations K 1. Complete one round of simulation 2. Generate the energy consumption vector 𝐄𝐄 3. Get the point vector P 4. Run the sorting and selection algorithm ͷǤ Get the sorted list of users: 𝐸𝐸0∗  ͸Ǥ  ‡––Š‡ƒ••‹‰‡–˜‡…–‘”ǣ𝑌𝑌0∗  Main Steps

7. while k ≤ K ƒ†𝐵𝐵𝑘𝑘 ≥ Ͳdo ∗ ͺǤ ’†ƒ–‡𝐸𝐸𝑘𝑘 = 𝑈𝑈(𝑌𝑌𝑘𝑘−1 ) a. Run the sorting and selection algorithm ‹Ǥ Get the sorted list of users: 𝐸𝐸𝑘𝑘∗  ͻǤ ‡––Š‡ƒ••‹‰‡–˜‡…–‘”𝑌𝑌𝑘𝑘∗  10. For ∀𝑖𝑖 ∈ 𝐼𝐼: 𝑘𝑘 𝑘𝑘−1 ii. If 𝑒𝑒𝑖𝑖𝑗𝑗 ∗ ≤ 𝑒𝑒𝑖𝑖𝑗𝑗 ∗

𝑘𝑘 𝑘𝑘+1 𝐾𝐾 iii. Set 𝑦𝑦𝑖𝑖𝑗𝑗 ∗ = 1, 𝑦𝑦𝑖𝑖𝑗𝑗 ∗ = 1, …,𝑦𝑦𝑖𝑖𝑗𝑗 ∗ = 1

b. Ž‡–𝐵𝐵𝑘𝑘 = 𝐵𝐵 − ∑𝑖𝑖 ∑𝑗𝑗 𝑝𝑝𝑖𝑖𝑗𝑗 ∗ 𝑦𝑦𝑖𝑖𝑗𝑗 ∗ 11. Set k= k+1 12. Return 𝐸𝐸 ∗ , 𝑌𝑌 ∗

Fig. 7. A search heuristic for incentive allocation problem

5. Numerical Demonstration and Results This section provides the numerical results of running the integrated system model, control optimizer and behavioral component in order to incentivize users toward more energy efficient travel alternatives. We first provide the details on how we run the SM followed by the discussions on the effects of incentive allocations in the D.C.-Baltimore area. 5.1. Scenario Definition: Real-World and Real-Time Incidents from the Data Infrastructure The simulations to evaluate the effectiveness of control strategies are set up as follows: • Agents are generated from a population synthesizer based on American Community Survey (ACS) data. Likely adopters who will receive information and incentives under the control optimizer are generated using models developed based on a large-scale market survey. • The traffic and energy simulation models are calibrated and validated for each of the six weekday scenarios (Monday to Friday, and a weekend day). • Simulation scenarios consist of incidents sampled from the historical dataset that the research team maintains in the Regional Integrated Transportation Information System (RITIS). Incident events include collision, disabled

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vehicle, vehicle on fire, injury/fatal accidents, and obstructions. The duration and capacity decrease of the incidents are observed and simulated in the model. • The system model simulation incorporates over 5 million travelers in the entire Washington D.C. and Baltimore metropolitan regions. Among them, about 20% are likely adopters and will likely take incentives into consideration when planning their trips. • The numerical demonstration focuses on PM Peak travels. To optimize the energy use, the budget (PM peak period only) for the incentive program is set to $30,000 (around a $6.8 million annual budget).

5.2. Simulation Results and Findings The integrated SM-CO simulation suggests that 7.5% of the agents (37.5% of the adopters) will take the incentive reward points and choose energy-efficient travel alternatives. At the presumed budget level, the system can achieve 8.7% energy use reduction (energy saving by place is illustrated in Figure 9a). Most monetary incentives are allocated to the region that is most influenced by the simulated incidents and to population-dense areas (shown in Figures 8a and 8b).

(a) Percentage of Energy Saving at Each Traffic Analysis Zone(left) (b) The Allocation of Monetary Incentives by Home Location(right) Fig. 8. Energy savings and incentive allocation under the control optimizer.

The agent-based simulation framework coupled with the Control Optimizer (CO) can simulate and optimize individual-level behavioral shifts and incentivization. Figures 10a, 10b, 10c, and 10d illustrate individualized results for adopters who are incentivized to use walk/bike, eco-routing, or public transit, and to change departure time, respectively. Transit options are well-received in the Washington and Baltimore urban areas, where frequent transit services are readily available, while departure time and routing adjustments are more typically seen in suburban and rural areas where travelers’ trip lengths tend to be longer. According to our simulation, among all the identified likely adopters who would be incentivized to change their travel behavior, 18% would shift to public transit and 32% would adjust their departure times. Another 26% would switch to ride-sharing, either by carpooling/vanpooling or by using ride-sharing services such as UberPOOL and Lyft Line. While 21% would stick with driving, they would follow ecorouting guidance. The final 3% would choose active travel modes (walk and bike) to complete their relatively shorter trips, which most likely occur in an urbanized area.

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a. Individuals Incentivized to Select Walk and Bike

b. Individuals Incentivized to Select Eco-Routing

c. Individuals Incentivized to Select Public Transit

d. Individuals Incentivized to Change Departure Time

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Fig. 9. Individual behavioral responses under the control strategy.

5.3. Comparison between Scenario I and II: In this section, we have conducted the analysis on the same model of the Extended PM Peak Hour (3:00 pm to 9:00 pm) for a representative day, in order to make a fair comparison between the two approaches. Overall, the results show that the search heuristic developed under the second scenario can achieve higher energy saving potential, compared to the first method. In particular, simulating under the same model settings (Extended PM Peak Hours, 20% penetration rate, and 30,000$ budget constraint for the modeled period), the developed search heuristic is able to reach a system-wide energy saving of 12.5%. It is a significant improvement, compared to the 8.7% reduction in energy use. Figure 10 displays the simulated additional energy savings achieved in different geographical locations in the network.

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Fig. 10. Additional Energy Savings (in Percentile) from Geographical Locations (Analyzed Based on Trip Origins)

5.4. Multidimensional Travel Behavioral Responses Finally, we assess the effectiveness of controls on the travel behavioral adjustments of the non-user travelers. The 20% penetration rate is significant enough to cause noteworthy changes in the overall traffic conditions. The changes in the traffic conditions are likely to stimulate behavioral adjustments. Figure 11 illustrates the multidimensional behavioral responses. In each subfigure, the size of the circles represents the number of agents seeking different behavioral changes. Overall, 51.2% of the non-user travelers consider changing their travel behavior and routing changes dominate the dimensions. It is seen that most significant routing behavioral changes occur in the urbanized areas and densely-populated areas. This conforms to the finding on the estimated energy savings. While not a lot of departure time changes has been predicted, those agents searching/changing departure times align on major freeway corridors, such as the two beltways for the Capital and Baltimore, the I-270 corridor, and the I-66 corridor. Simulated commuters traveling through these corridors are more willing to look into departure time changes in order to avoid congestion. Finally, a slight percentage of travelers seek modal shifts in highly urbanized areas and where transit services and park-and-ride facilities are more readily available.

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51.2% of total agent

43.7% of total agent

7.1 % of total agent

0.4% of total agent

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Fig. 11. Effect of Controls on the Simulated Agent-Based Behavior (Non-User Agents).

6. Conclusions This paper illustrates the development of an integrated personalized and real-time traveler information and incentives scheme. The scheme encompasses a system model (SM) consisting of integrated person-level travel behavior, dynamic traffic, and energy use simulators, and a control optimizer (CO) based on behavior research, travel intent prediction, and incentive optimization innovations. This integrated system can guide travelers, through personalized signaling and incentives, to individually adjust their mode, departure time, route, and driving style choices, and to collectively minimize energy consumption in a multimodal transportation system. The paper demonstrated the system in a large-scale, real-world case study in the Washington, D.C.-Baltimore metropolitan region to highlight its capabilities and applicability to address real-world, energy-efficient mobility challenges. Through credible behavior research on over one thousand D.C.-Baltimore residents, a personalized and multidimensional incentive structure was designed through a principal-agent theoretical model. It was then linked to a computationally efficient and robust control optimizer for system-level incentive optimization. This innovative solution can help existing incentive systems operate in a much more effective and efficient manner. The case study demonstrates that with 7.5% of agent adoption, the energy savings from a scheme prototype can be as high as 12.5%, given the particular scenario analyzed by the numerical example. Personalized and multi-dimensional behavioral changes result from the incentivization. For example, travelers can be incentivized to avoid peak hour departures instead of modal shifts to transit, if their trip origin-destination is not well-served by a transit service. The research team has also developed the next-generation integrated transportation system simulation models (SM) and control optimizer (CO) with superior computational efficiency. Several unique innovations ensure this computing feature, including: the behaviorally sound behavioral user equilibrium (BUE) and its embedded model convergence property; the parallelized computing of the traffic simulator; the efficient optimization algorithm; and the cloudcomputing deployment. The computational performance based on cloud-computing highlights the capability of the

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incentive scheme in handling a wide spectrum of real-time applications. It will be especially suitable for applications in active and integrated traffic management, and advanced transportation congestion and travel demand management. The main contribution of the research is two-fold: • The paper developed and deployed one of the first integrated modeling systems for advanced personalized incentive signal delivery in transportation. The system supports real-world assessment of incentive programs and provides an innovative solution for system energy and congestion reduction, and advanced travel demand management. • Theoretical models have been developed and empirically tested to quantify and optimize the amount of incentives using a large and representative sample collected in the D.C. and Baltimore regions. It paves a theoretical foundation for existing and future travel-incentive programs. As an immediate next step, the authors plan to deploy and test-run the technology in a smartphone-based app platform. The testing data, especially the actual user behavior data, will be valuable to verify and re-calibrate the behavioral prediction models, improve the incentive mechanism, and further personalize the information and incentive structure. Another limitation of the study is that gamifications and attitudes toward risk are currently missing and should be explored and incorporated in the overall framework. Theoretical research can also examine the moral hazard in transportation mobility behavior. For instance, hidden information on those who habitually prefer public transit is often hard to detect and may decrease the system efficiency of running such an incentive program. Acknowledgements This research is financially supported by Maryland State Highway Administration (MD-SHA), and the Department of Energy ARPA-E TRANSNET Program. The opinions in this paper do not necessarily reflect the official views of MD-SHA, U.S. DOE, or ARPA-E. They assume no liability for the content or use of this paper. The authors are solely responsible for all statements in this paper. References Brownstone, D. & Golob, T. F. (1992). The effectiveness of ridesharing incentives: discrete-choice models of commuting in Southern California. Regional Science and Urban Economics, 22(1), 5-24. Diamond, D. (2009). The impact of government incentives for hybrid-electric vehicles: Evidence from U.S. states. Energy Policy, 37(3), 972983. Daganzo, C. F. (1995). The cell transmission model, part II: network traffic. Transportation Research Part B: Methodological, 29(2), 79-93. Dixit, V., Ortmann, A., Rutstrom, E.E., & Ukkusuri, S. (2017). Experimental Economics and choice in transportation: Incentives and context. Transportation Research Part C, 77, 161-184. Diubin, G. & Korbut, A., (2003). The average behaviour of greedy algorithms for the knapsack problem: general distributions. Mathematical Methods of Operations Research, 57(3), pp.449-479. Diubin, G. & Korbut, A., (2008). Greedy algorithms for the minimization knapsack problem: Average behavior. Journal of Computer and Systems Sciences International, 47(1), 14-24. Frey, H, C. & Liu, B. (2013). Development and evaluation of a simplified version of MOVES for coupling with a traffic simulation model. Proceedings, 91st Annual Meeting of the Transportation Research Board, Washington D.C. (Paper 13-1201). Fujii, S. & Taniguchi, A. (2006). Determinants of the effectiveness of travel feedback programs – A review of communicative mobility management measures for changing travel behaviour in Japan. Transport Policy, 13(5), 339-348. Garling T. & Satoshi, F. (2009). Travel behavior modification: Theories, methods, and programs. The Expanding Sphere of Travel Behavior Research. Kitamura, R., Yoshii, T., Yamamoto, T. Eds., 97-128. Emerald. Bingley, UK. Georgia Department of Transportation (2015). GIMME FIVE, Georgia Commute Options. https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&ved=0ahUKEwi6lqyimoLTAhXID8AKHfBJANEQFggnMAI&ur l=https%3A%2F%2Fgacommuteoptions.com%2Fcontent%2Fdownload%2F17506%2F145777%2Ffile%2F%245%2520a%2520Day%2520P ress%2520Release_FINAL.pdf&usg=AFQjCNEu6xQqfskRVXA_ZNt9ukNdI9IKFQ&sig2=gqKJlNvU0qRQ_o5E5sAQZA Jacobs, H. E., Fairbanks, D., Poche, C. E., & Bailey, J. S. (1982). Multiple incentives in encouraging car pool formation on a university campus. Journal of Applied Behavior Analysis, 15(1), 141-149. Hu, X., Chiu, Y.C., & Zhu, L. (2015). Behavior insights for an incentive-based active demand management platform. International Journal of Transportation Science and Technology, 4(2), 119-134.

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