Exploring usage patterns and safety perceptions of the users of electric three-wheeled paratransit in Patna, India

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Exploring usage patterns and safety perceptions of the users of electric threewheeled paratransit in Patna, India Shiv Priye, M. Manoj

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Department of Civil Engineering, Indian Institute of Technology Delhi, New Delhi 110016, India

ARTICLE INFO

ABSTRACT

Keywords: Electric vehicles Informal public transport User characteristics Travel behaviour Grey relation analysis

Electric three-wheelers (popularly known as electric rickshaws) are battery-powered vehicles that have emerged in many Indian cities as an environment-friendly and energy-efficient paratransit mode of transport. The government of India is developing policies for the broader adoption of the mode to promote sustainable mobility and accessibility. While many Indian cities have been operating electric rickshaws, little is documented about the usage patterns of this mode. An investigation into the usage patterns and safety perceptions of the riders would provide essential inputs to the planning and design of electric rickshaw services for a city. Using a primary data collected from Patna, India, the study explores the travel patterns and safety perceptions of electric rickshaw riders. Statistical models were developed to investigate the effects of socio-demographics and travel context attributes on trip rate, mode replacement and continuation of the use of electric rickshaws in the future. The analysis reveals that electric rickshaws replace a majority of trips made on conventional (gasoline-based) autorickshaws. It is found that electric rickshaw is a popular mode choice for educational—and shopping-related trips and students are the frequent users. Further, to identify the most relevant safety issues related to electric rickshaws, grey relation analysis (GRA) was applied to the scaled items. The results disclose that the users are highly concerned over the light body, solid covers and railings, and about the rear-end protection of the electric rickshaws. The on-board safety of small children is also of high concerns. Individuals who opine that electric rickshaws require rear-end protection or need solid covers and railings will be less likely to use this mode in the future. Further, females are less likely to continue the use whereas students would use electric rickshaws in the future, all else being equal. The findings of the study would help develop plans and policies for the improvement of electric rickshaw services to have a sustainable public transportation system that is less dependent on conventional fuels.

1. Introduction Urbanization and motorization have put tremendous pressure on the environment and its resources. Accordingly, greenhouse gas emissions and energy security have become a significant challenge for governments and scientists across the world (IPCC, 2007). It has been predicted that the reserved fuel in the world would get depleted by 2050 (IEA and OECD, 2003; Shafiee and Topal, 2009). The Paris Agreement of the United Nations is committed to addressing climate change (UNFCC, 2015). To minimize the negative externalities of the transportation sector, researchers are of the opinion that sustainable public transport can play a significant role (Maitra and Sadhukhan, 2013; Pérez et al., 2015; Jain and Tiwari, 2016). However, in India, due to the insufficient and poor quality public transport, medium–, and higher-income group travelers are shifting to private modes (Badami

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and Haider, 2007), and the urban poor relies on paratransit to fulfill their travel needs (MoUD, 2008). The paratransit modes (especially three-wheelers) are the key mobility options for the urban poor (Mani and Pant, 2012; Harding et al., 2016). It is found that paratransit index, i.e., the number of paratransit per 10,000 individuals, is higher in cities that lack proper public transport (MoUD, 2008). According to Mani et al. (2012), Tier-I (population above 4 million) and Tier-II cities (population between 1 and 4 million) have nearly 50,000 and 15,000–30,000 three-wheeled auto-rickshaws respectively which means on an average, 4 to 16 auto-rickshaws serve the travel needs of every 1,000 people of Tier-I and Tier-II cities. This trend shows the importance that paratransit have in Indian cities. However, the Indian government's policy initiatives like Jawaharlal Nehru National Urban Renewal Mission (JNNURM, 2005) and the National Urban Transport Policy (NUTP, 2006) have not emphasized on

Corresponding author. E-mail address: [email protected] (M. Manoj).

https://doi.org/10.1016/j.cstp.2020.01.001 Received 28 November 2018; Received in revised form 1 January 2020; Accepted 2 January 2020 2213-624X/ © 2020 Published by Elsevier Ltd on behalf of World Conference on Transport Research Society.

Please cite this article as: Shiv Priye and M. Manoj, Case Studies on Transport Policy, https://doi.org/10.1016/j.cstp.2020.01.001

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Fig. 1. Electric Three-Wheelers in Patna.

indicating safety issues. Understanding the safety issues of electric rickshaws, many district authorities in India have prohibited the use of this mode for school-travel (e.g., Government of Bihar, 2016). With the abrupt increase in the number of electric rickshaws and accidents, there is a serious debate going on whether to ban or regularize these vehicles (Padmanabhan et al., 2014; Marwah and Bawa, 2016). However, before taking any leap in that direction, extensive research is needed to have a proper understanding of the users of this mode; user travel context and their perceptions of the safety of this mode. An investigation into these aspects would provide essential inputs to planning and policy because of the following reasons. First, understanding the users' profile and their socio-economic status would inform who is willing to use electric rickshaws and their ability to pay for the use (in case there is a change in fare policy). The knowledge would help decide future policy for electric rickshaws so that the demand is sustained or improved. Second, an understanding of the travel characteristics would inform the travel demand for electric rickshaws in a city and the spatial extent for which the people want to use the system. The origin–destination scenario would help uncover the network usage of electric rickshaws and help locate the charging facilities and maintenance stations. Third, research is also necessary to investigate the safety issues concerning electric rickshaw users as the safety of life and goods are directly related to transport safety (Márquez et al., 2014). Studies have found that users’ perception of safety plays a significant role in the choice of mode of travel, particularly public transport (Delbosc and Currie, 2012). Currently, in India, electric rickshaw parts are imported from neighboring countries and are locally assembled without developing/following design guidelines, leading to suspicions regarding the safety of the vehicle from the structural and technical point of views (Bhasin and Bhardwaj, 2014; Padmanabhan et al., 2014). Also, electric rickshaws run on mixed traffic context, making the passengers and by-passers vulnerable from a safety standpoint (see Fig. 1). Research on these aspects would add to the knowledge on this important and growing transportation mode and for proposing policy measures to augment the same. Furthermore, research of the kind presented here would help contribute to the agendas of “Smart Cities Mission” of Government of India since last mile connectivity is one of its primary focuses and electric rickshaw could offer energy-efficient and sustainable mobility option to ensure seamless travel. Given the background, the objectives of the research are as follows.

improving the paratransit sector. Recently, the Working Group on Urban Transport emphasized the need for technology development and enhancement of paratransit services (NTDPC, 2012). It is recognized that paratransit has immense potential to provide clean and low-emission mobility options (Kumar et al., 2016). However, very few studies have been undertaken in this area (Kunhikrishnan and Srinivasan, 2018). The Smart Cities Mission of Government of India has stressed upon promoting sustainable modes of transportation. The Smart City concept is proposed to improve the life quality of citizen by enabling comprehensive city (re)development and upgrading programs (Zubizarreta et al., 2015). The practice of an attractive and environment-friendly mode of paratransit for last-mile connectivity is one of the aims of the urban mobility plan of “Smart Cities Mission” (Smart Cities Mission, 2018). Given this, battery-operated electric three-wheelers or 'Electric rickshaws' (see Fig. 1) have entered urban India. Electric rickshaw is a battery-powered three-wheeled vehicle and comes under the purview of paratransit services. The Indian Parliament has legalized electric rickshaws by passing an amendment to the Motor Vehicles (Amendment) Bill, 2015. According to the Indian Motor Vehicles (Amendment) Act, 2015, the electric rickshaw is defined as “a special purpose battery operated vehicle having three wheels and power not exceeding 4,000 W, to carry passengers for hire” (Motor Vehicles (Amendment) Act, 2015). These vehicles are powered by brushless DC motors for vehicle propulsion and the energy is supplied by conventional lead-acid batteries (Majumdar and Jash, 2015). In India, where there is a lack of proper infrastructure to accommodate full-fledged public transport, electric rickshaws have come up as an affordable mode of paratransit for commuting. These vehicles have come out at the time when debates are going on regarding energy security and reduction of greenhouse gas emissions. Electric rickshaws are considered to be affordable and environmental-friendly and have the potential to reduce carbon-footprint from passenger transport activities, thus giving stiff competition to other popular paratransit modes such as conventional (gasoline-based) three-wheeled auto-rickshaws, mini buses, among others. 1.1. Problem identification Although electric rickshaw has certain advantages over other modes, it presents some major challenges to transportation planners and policymakers regarding its impact on the transportation system and road safety. The recent Government of India reports show that electric rickshaws are involved in nearly 380 (registered) fatal accidents in India in 2016 (MoRTH, 2017). It is the first time that the Government of India has collected data regarding electric rickshaw-related accidents,

i. To understand the socio-demographic characteristics of electric rickshaw users and their electric rickshaw use patterns. ii. To investigate the mode choice of respondents if electric rickshaws are not available. 2

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iii. To explore the safety perceptions of electric rickshaw users and to understand the role of safety perceptions on the future use of electric rickshaws.

income groups and it creates urban employment, especially for the unskilled persons. Some general negative perception about paratransit, as highlighted by researchers, include contribution to traffic congestion; safety issues; emission of harmful pollutants; association with illegal activities and the impacts on price irregularities (Silcock, 1981; Cervero, 1991, 2000; Cervero and Golub, 2007; Joewono and Kubota, 2007a; Kumarage et al., 2010; Min, 2011; Phun and Yai, 2016). Since electric rickshaw is a new product in India, besides a few market reports (Harding and Rojesh, 2014; Chandran and Brahmachari, 2015; Marwah and Bawa, 2016), limited empirical research exists within the peer-reviewed literature. Some of the past studies are related to the operational and technical aspects of electric rickshaws (Harding, 2014; Singh, 2014; Majumdar and Jash, 2015; Sreejith and Rajagopal, 2016; ICLIE, 2017; Vashist, 2018). The existing literature concludes that electric rickshaws could provide environmental-friendly, energyefficient and cost-effective mobility in cities and towns of India (Rajvanshi, 1997, 2002). It can be seen that not much emphasis has been given to explore the socio-economic profile, travel behavior, and safety perception of electric rickshaw users.

The study combines descriptive analysis (univariate) and regressionbased approaches (to control for confounders) to understand users’ profiles and their travel patterns. In order to explore users’ profiles and to provide broad insights into the travel patterns, univariate descriptive analysis is undertaken. Further, for travel behaviour analysis, regression-based statistical approaches are adopted as they could reveal the strengths and association of different explainers in the presence of other variables. The paper explores travel patterns and safety perceptions of electric rickshaw riders through different indicators. To understand trip demand, a logistic regression model is applied to classify the riders based on trip demand. To investigate the most relevant safety-related aspects of electric rickshaws, grey relation analysis is adopted and using the ranked statements, logistic regression is implemented to understand the influence of safety features on the continuation of the electric rickshaw. Further to explore the mode preferences of riders when electric rickshaws are not available, multinomial logistic regression is applied due to its ability to handle nominal data. The remaining sections of the paper are structured as follows. The next section gives an overview of paratransit. Following that, Section 3 introduces the study area and survey design. Subsequently, the sociodemographic characteristics of electric rickshaw users and their usage patterns are discussed in Section 4. This section also investigates the safety perceptions of users about electric rickshaw and also explores the factors that influence the continuation of the use of electric rickshaw. The next section summarizes the paper and highlights the main findings of the study. The planning and policy implications of the research are highlighted in Section 6. The final section discusses the scope of further research in this area.

3. Survey design and data collection A survey was conducted in Patna to capture the travel characteristics and safety perceptions of electric rickshaw users. Patna is one of the “100 Smart Cities” proposed under the “Smart Cities Mission” of the Government of India (Smart Cities Mission, 2018). The Patna Municipal Corporation (PMC) has a population of 1.68 million (Census of India, 2011). The area covered in PMC is 99.45 sq. km and the road density is 13 km per sq. km. The road network is inadequate since only 10% of the area available for circulation against the standard requirement of 15–20% (Government of Bihar, 2011). In Patna, the formal public transport service is not adequate (Sinha et al., 2017) and this has given rise to the proliferation of informal public transport (privately operated mini buses and three-wheelers) which has a modal share of 27% (TERI, 2014). The recently emerged electric rickshaws confirm to the operational features of paratransit modes. That is, electric rickshaws do not adhere to fixed routes and schedules, and do not have designated stops. Electric rickshaws follow 'demand-responsive' operations in Patna. The electric rickshaws plying in Patna can accommodate a maximum of four adults in addition to the driver. The cost of fare for electric rickshaws in Patna as compared to that of conventional fuel-based auto-rickshaws varies by regions (i.e. in some locations it is slightly higher whereas in other places it is the same). However, it is higher than that of mini buses and cheaper than that of human-powered pedal-rickshaws. This research employed a paper-based questionnaire survey to gather travel behavior and perceptions related information from a sample of electric rickshaw users in Patna. The questionnaire comprised of three sections. In the first section, the respondents were asked to rate their levels of agreement with various safety-related attributes on a five-point Likert-type ordinal scale (where “1″ indicated “strongly disagree” while “5” indicated “strongly agree”). Also, the respondents were asked whether they continue the use of electric rickshaws in the future. In the second section, the respondents were requested to provide details of their last electric rickshaw trip, such as trip purpose, trip origin and destination, the choice of alternative mode for the previous trip in case electric rickshaws were not present, weekly trip rate on electric rickshaw and so forth, and the final section solicited their sociodemographic characteristics. The questions related to travel characteristics and user profile were kept last based on the conclusions of Rastogi and Rao (2002) and Suman et al. (2016). For investigating users’ perceptions related to safety, a thorough literature review was carried out. Attributes such as riding behavior, driver skills, speed, stability, structure and body of the vehicle, safety features, traffic condition and infrastructure are some of the critical aspects related to transportation safety as evident from previous studies (Joewono and Kubota, 2006; Weinert et al., 2007; Ma et al., 2010; Yavuz and Welch,

2. An overview of paratransit Public transport can be classified into two main categories: formal and informal (Kumar et al., 2016). Government organizations generally operate the formal transit. City transport infrastructure is planned and designed according to the requirements of the formal public transport modes such as bus rapid transit systems, rail rapid transit systems, among others (Mohareb and Felix, 2017). On the other hand, informal transit is usually owned and operated by private operators and individuals. Informal public transport is a demand-responsive service and does not adhere to fixed routes or schedules. Vehicle types that suit the local user requirements and infrastructure – e.g., three-wheeled pedal rickshaws, three-wheeled auto-rickshaws, taxis, and mini buses – are generally used as informal transit services (Gadepalli et al., 2018). In scholarly literature, such informal modes of (public) transport are commonly referred to as ‘paratransit’, ‘low-cost transport’, ‘intermediate public transport', ‘artisanal transport’ 'flexible transport service', 'demand responsive transport', 'third world transport', 'alongside transit' or 'shared taxis' (Cervero, 2000; Lave and Mathias, 2000; Phun and Yai, 2016; Schalekamp, 2017). The concept of paratransit is different in developed and developing countries (Silcock, 1981; Joewono and Kubota, 2007b; Ghosh and Kalra, 2016). “Paratransit usually refers to 'demand responsive' and 'doorto-door transport service', provided exclusively for the elderly and people with disability” (Cervero and Golub, 2007; Davison et al., 2014; Fei and Chen, 2015; Neven et al., 2015) in developed countries. Whereas in developing countries, due to lower living standard and poor public transport services, paratransit serves the functions of formal public transport (Lave and Mathias, 2000; Cervero and Golub, 2007; Behrens et al., 2017; Nugroho and Zusman, 2018). There are several advantages of paratransit as evident from the literature. Paratransit fills the service gap created due to inadequate public transport; it is flexible and sensitive to the changing market; it is affordable by low–and medium3

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Table 1 Description of Safety Attributes with their abbreviations. Codes

Attributes

Statements*

A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12

Pavement condition Light Body Solid cover and Railing Driver's Behavior Turning Points Rear-end protection Feels unsafe when fast-moving vehicles pass closely Threat of snatching Seat Belts Unsafe for small children Unsafe in a mixed traffic situation Unsafe during night time

The body of electric rickshaw shakes while driving on undulating surfaces Electric rickshaw will have a severe impact in case of accidents due to its very light body Electric rickshaw must have solid covers and railings Drivers drive recklessly and violate traffic rules Fear of over-turning comes when electric rickshaw takes a sharp turn at curves There should be protection from rear-end collision There is a feeling of unsafe when high-speed vehicles pass electric rickshaw very closely Electric rickshaw is unsafe at signals due to the threat of snatching, robbery, etc. There must have seat belts for every rider Electric rickshaw is unsafe for small children who are traveling alone Electric rickshaw is unsafe in a mixed-traffic situation Traveling in an electric rickshaw during night time is unsafe

*Likert-type five-point ordinal scale was used (1 = Strongly Disagree to 5 = Strongly Agree)

respondents were graduates or qualified above it. Thirty-five, 11 and 7 percentages of respondents were completed intermediate (12th), matriculation (10th) and primary school, respectively. In the occupation category, about 37% of respondents were doing jobs/businesses or selfemployed. It is interesting to observe that students comprised a significant percentage of the populace (33%) riding electric rickshaws in Patna. In this category, ‘others’ comprised of about 30% respondents which mostly include housewives and retired individuals. In the household monthly income category, about 5% of respondents are from ‘below 10 k’ income (INR - Indian Rupees) category. This suggests that low-income individuals depend less on electric rickshaws, probably due to the availability of comparatively cheaper modes such as mini buses and bicycles in Patna. About 11%, 15%, 21% and 25% respondents were from 10 to 20 k, 21–30 k, 31–40 k and 41–50 k INR income categories, respectively. Similarly, about 23% of respondents were from ‘more than 50 k’ INR category. In the vehicle-ownership category, about 39% of respondents owned cars and about 45% owned motorized twowheelers (MTWs). Here, it may be noted that vehicle-ownership includes pre-owned vehicles also. In line with expectations, the proportion of car owners is low in the sample. To understand the effect of income on vehicle ownership (car and motorized two-wheeler), logistic regressions were run between vehicle ownership (own or not) and monthly household income. In the case of car ownership, the effect of motorized two-wheelers was also considered. It can be seen from Table 2 that as income increases, the probability of owning a car increases. It is similar to the observation made by Dargay (2001) and Suman et al. (2016). However, as the number of motorized two-wheelers at home increases, the probability of having a car declines. Table 2 also shows the same trend with motorized two-wheelers (MTWs) as seen in car ownership model but the probability of owning an MTW is higher within the (income) range for medium-income groups.

2010; Mani et al., 2012; Rana et al., 2013; Lv et al., 2015; Haustein and Møller, 2016). Moreover, to understand the ergonomics of the electric rickshaw, several rounds of discussions with the manufacturers and experts in the transportation safety field were also undertaken. In this regard, about 20 commuters belonging to different age-groups were also interviewed to know their experiences while commuting on electric rickshaws. Several reports related to accidents involving electric rickshaws were also studied to observe the reasons for the crashes. Subsequently, incorporating all relevant attributes, a pilot survey was conducted among 40 electric rickshaw users in Patna to identify the possible problems with the survey instrument. The final survey instrument was designed with minor modifications based on the feedback from the pilot survey. The safety attributes considered for the primary survey are summarized in Table 1 with their specific codes and descriptions. A paper-based primary questionnaire survey was then carried out from March to April 2018, by following a face-to-face interview method (Stopher, 2012) with the help of trained volunteers. A simple random sampling technique (Cochran, 2007) was adopted in the study. The surveyed commuters were approached – (i) onboard the randomly selected electric rickshaws and (ii) at the different locations of PMC where electric rickshaws were observed to be plying frequently. The surveyed locations in PMC are shown in Fig. 2. Five hundred and sixty electric rickshaw users were approached for the survey. However, only 410 users participated (response rate of 73.21%). It was found that 22 questionnaire forms had some missing or multiple responses which were then removed and after refinement, 388 completed responses were obtained. Since the daily electric rickshaw ridership for Patna is not available, a sample of 388 respondents is found to be statistically acceptable as per Cochran's formula (Cochran, 1977) by assuming maximum variability and taking 95% confidence level with ± 5% precision. 4. Empirical results

4.2. Travel characteristics of electric rickshaw users

This section summarizes the empirical results. In section 4.1, a brief discussion about the socio-demographic profile of the sample is provided. Following this, the travel patterns of the users are discussed. Section 4.3 explores the respondents' perceptions of the safety of electric rickshaws and Section 4.4 develops a statistical model to investigate the factors shaping the continuation of the use of electric rickshaws.

Table 3 shows the travel characteristics of electric rickshaw users in Patna. About 78% of the respondents make at least two (electric rickshaw) trips a week. The information on a recently completed trip on electric rickshaw suggests that the rickshaw is mainly used for nonwork travel, particularly for shopping. It can also be seen that nearly 30% of trips made on electric rickshaw are for education-related activities. Surprisingly, the electric rickshaw primarily serves as a main mode of travel, and about 77% of the respondents used it for accessing main activity locations in their recent trip. Finally, had electric rickshaws been not available, many respondents would have chosen conventional three-wheeled auto-rickshaw followed by mini-buses1

4.1. Socio-demographic profile Among the usable data, 52% respondents were male and 48% were female. Age distribution suggested that the respondents mostly belonged to the age band 25–55 years (nearly 58%). Thirteen and 15 percentages of respondents were of below 18, and 18 to 24 years of age band, respectively. In the education category, about 45% of

1

4

Minibus is 12–20 seater, diesel-run vehicle in Patna

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Fig. 2. Survey locations in PMC. Table 2 Vehicle Ownership versus Income Categories. Variable

Car Ownership β

Household Monthly Income (₹) Up to 10 k Between 10 and 20 k Between 21 and 30 k Between 31 and 40 k Between 41 and 50 k More than 50 k MTW Ownership Constant Number of Observation Pseudo R2 Log likelihood

Table 3 Travel characteristics of electric rickshaw users.

t

~ 1.251 1.11 1.276 1.15 1.750 1.61 3.434 3.23 5.090 4.69 −1.383 −4.79 −2.742 −2.66 388 0.3591 −166.49539

MTW Ownership Prob.

0.266 0.251 0.107 0.001 0.000 0.000 0.008

β

t

~ 1.519 1.87 2.212 2.79 2.698 3.43 2.079 2.68 0.950 1.21 – – −2.079 −2.77 388 0.0806 −244.96685

Travel Characteristics

Classification

Frequency

Percentage

Prob.

Trip Rate

86 114 108 80

22.16 29.38 27.84 20.62

0.062 0.005 0.001 0.007 0.228 – 0.006

Trip Purpose

0–1 Trip/week 2–3 Trips/week 4–5 Trips/week More than 5 Trips/ week Work Business Education Recreation/Social Shopping Others Main mode Access/Egress mode Auto-rickshaw

58 18 112 46 100 54 297 91 160

14.95 04.64 28.87 11.86 25.77 13.92 76.55 23.45 41.24

Pedal-rickshaw Mini Bus Car MTW Walk Bicycle Others* Yes

36 63 32 18 25 24 30 122

09.28 16.24 08.25 04.64 06.44 06.19 07.73 31.44

Mode electric rickshaws used as Alternate mode chosen in absence of electric rickshaws

(another form of paratransit in Patna). Interestingly, many respondents prefer to walk and bicycle than riding a motorized two-wheeler in case electric rickshaws are not available. In this study, the trip distance information is deduced from the origin–destination details captured about the recent trip made on an electric rickshaw. The average trip length is observed to be 2.1 km which indicates that the respondents use electric rickshaw for short trips.

Continue the use of electric rickshaws

*includes pillion riding, shared ride, and official vehicles, etc.

frequently. This finding is of relevance for promoting policies to improve electric rickshaw ridership. In line with the general wisdom, individuals of car-owning households make fewer trips on electric rickshaws.

4.2.1. Analysis of trip rate This section investigates the intensity of electric rickshaw travel (trip rates) in detail. Table 4 presents the estimation results of the logistic regression model for trip frequency. The reason for choosing binomial regression is the variability of the data. The data did not reveal the suitability of conventional regression or Poisson family models. Rather, the data supported the estimation of a binary logit model that could classify riders into two segments: riders making ‘more than 3 trips/week and those making ‘less than 3 trips/week. The model suggests that females make more trips than males, probably an indication of the lack of access to household vehicles to women. The occupation status of an individual suggests that students use electric rickshaws

4.2.2. Mode replacement Table 3 shows the distribution of modes that respondents would have used in the non-availability of electric rickshaws. The information is pertaining to the last trip on an electric rickshaw. It can be seen that about 42% of electric rickshaw trips would have been replaced by autorickshaws. A multinomial logistic regression model was estimated to explore 5

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Table 4 Trip Rate Analysis. Variable

β

t

Prob.

Gender (Female) Occupation Service/Job/Business/Self-Employed Student Others Car ownership (yes) Constant Number of Observation = 388 Pseudo R2 = 0.2460 Log likelihood = -202.63372

0.633

2.15

0.031

~ 2.747 −0.240 −1.313 −0.617

8.22 −0.73 −4.68 −3.01

0.000 0.466 0.000 0.003

Table 5 MNL model for Mode choice in absence of electric rickshaws (Base = Mini Bus). Variable

Gender (Female) Trip Purpose Work/Office/Business Education Recreation/Social/Shopping/Others Car ownership (yes) Trip rate Less than 3 trips/week More than 3 trips/week Trip length Constant Number of Observation = 388 Pseudo R2 = 0.2063 Log likelihood = -494.07926

Auto-Rickshaw

Pedal-Rickshaw

Private Vehicle

t

Prob.

β

t

Prob.

β

t

Prob.

β

t

Prob.

β

t

Prob.

2.037

4.68

0.000

2.400

4.09

0.000

2.327

4.30

0.000

−0.511

−0.86

0.387

2.044

3.37

0.001

~ −0.944 0.012 1.046

−2.06 0.03 1.98

0.040 0.979 0.048

~ 0.121 1.257 1.840

0.12 1.41 2.94

0.905 0.158 0.003

~ −0.648 0.166 2.579

−0.87 0.25 4.34

0.382 0.801 0.000

~ 1.381 2.072 2.400

1.51 2.40 4.07

0.132 0.016 0.000

~ 0.226 −0.845 1.853

0.24 −1.28 2.88

0.808 0.202 0.004

~ 0.145 −0.297 1.004

0.32 −1.83 1.93

0.747 0.067 0.054

~ 0.159 −2.135 0.827

0.24 −5.00 0.83

0.813 0.000 0.407

~ −0.637 −0.192 −1.126

−1.02 −0.87 −1.50

0.310 0.382 0.134

~ 0.146 −0.958 −0.562

0.22 −3.16 −0.58

0.824 0.002 0.564

~ −2.847 0.067 −0.586

−3.13 0.30 −0.84

0.002 0.766 0.399

in responses obtained from electric rickshaw users, regarding their agreement with safety attributes, are shown in Fig. 3. Further, a statistical reliability test using Cronbach’s alpha coefficient was conducted on the responses related to the safety attributes to measure the internal consistency of the responses. The obtained Cronbach’s alpha coefficient of 0.864 (George and Mallery, 2003) indicated that the questionnaire could measure all the safety attributes in a meaningful way and there were significant differences among the ratings (gathered from electric rickshaw users) of safety attributes. Thereafter, in order to identify the most relevant safety issues related to electric rickshaws (based on the perceptions of the users), an attempt was made to apply grey relation analysis (Julong, 1989) to rank order the Likert-scale data (related to given safety attributes) of the given 388 respondents. Grey relation analysis (GRA) comes under a group of scientific analysis called multi-criteria decision-making (Roy and Basu, 2019), which has been successfully implemented in various fields of scientific research, especially in the analysis of safety perceptions (Hsu et al., 2010; Onyegiri and Oke, 2017). Since small sample size may influence the reliability and precision of the outcome, an important advantage of GRA approach over traditional statistics analysis is that it can analyze the ranking of small-sampled Likert-scale data very effectively (Hsu et al., 2010; Mazumdar, et al., 2010; Sadhukhan et al., 2014; Kumar and Bhattacharyya, 2017). The ‘grey relation grades’ of grey relation analysis rescale the values of different items, by taking the differences from the most favored (commonly, the maximum of the scale) and the range of the scale, so smaller grades reflect the lower importance assigned to the feature and values close to 1 indicate that respondents rated highly (close to the maximum of the scale) the feature. GRA method has emerged as a reliable approach to analyse the small-sampled rating data and the calculations are simple and straight forward (Cenglin, 2012; Kumar and Bhattacharyya, 2017). Besides, the grey relation analysis is distribution-free (Cenglin, 2012). Appendix A briefly summarizes the steps involved in GRA.

4.3. Safety perception of electric rickshaw users The users’ opinions regarding safe travel in electric rickshaws were also captured in the questionnaire survey. The survey participants were requested to express their perceptions regarding various safety-related attributes (see Table 1) on a five-point Likert-type scale. The variations

3

Others

β

the choice of alternative mode a rider would have chosen, had electric rickshaws were unavailable for the last trip. The model is summarized in Table 5. An important limitation of the model is that it does not control the cost of travel by different modes. The research data do not have cost information as some of the riders were unaware of the cost of travel for alternative modes in the pilot survey and therefore, it was excluded from the final questionnaire. In the table, the private vehicle category includes both car and MTW, and NMT comprises walking and bicycling. The alternative ‘others’ includes mode options such as pillion riding2, shared ride, and official vehicles3. The table reveals that females are less likely to opt for walking and bicycling if electric rickshaws were not available whereas they are more likely to choose autorickshaws and pedal-rickshaws. The influence of trip purpose implies that the respondents travelling for education-related activities (or students) are less likely to opt for auto-rickshaws in the absence of electric rickshaws - probably an indication of the impact of higher fare of autorickshaws. Individuals from car-owning households are more likely to shift to private vehicles, as implied by the magnitude and significance level of the estimate for car ownership variable. As trip length increases, the respondents do not prefer pedal rickshaws and NMT, and a unit change in trip length has a substantial impact on the choice of pedal rickshaw than NMT.

2

NMT

taking a lift on someone’s motorized two-wheeler government/organizational vehicles meant to transport their employees 6

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Fig. 3. Variation in responses regarding the level of agreement. Table 6 Ranking of Safety Attributes using GRA method. Attributes (with codes)

max Δi

min Δi

Grey Relation Grade (Γi)

Pavement condition (A1) 4 0 0.745 Light Body (A2) 2 0 0.862 Solid cover and Railing (A3) 4 0 0.789 Driver's Behaviour (A4) 4 0 0.639 Turning Points (A5) 4 0 0.665 Rear-end protection (A6) 4 0 0.730 Feels unsafe when fast-moving vehicle passes closely (A7) 4 0 0.683 Threat of snatching (A8) 4 0 0.659 Seat Belts (A9) 4 0 0.553 Unsafe for small children (A10) 4 0 0.839 Unsafe in a mixed traffic situation (A11) 4 0 0.656 Unsafe during night time (A12) 4 0 0.544 For reference data series (a0), the most favored responses were taken as ‘5′ Likert-scale value. From the columns (max Δi) and (min Δi) of the above table, (Δmax) = 4 and Global Minimum Value (Δmin) = 0 were obtained respectively

Rank* 4 1 3 10 7 5 6 8 11 2 9 12 Global Maximum Value

*Higher Γi value means higher rank

4.3.1. Identification of critical safety attributes The safety data obtained from the questionnaire survey was analyzed using GRA method as explained in Appendix A. Table 6 shows the final ranking of the measures related to safety. To capture the most critical attributes related to safety, threshold grey relation grade was estimated (Onyegiri and Oke, 2017). The estimated grade is 0.697 and utilizing this cut-off value, five major critical attributes are identified and are shown in Table 6 (see codes A1, A2, A3, A6 and A10). It may be observed that the most relevant safety-related attributes are related to the structure of electric rickshaws. Electric rickshaw users perceive the light body of electric rickshaw as the most critical attribute related to safe travel. The second most crucial element is the open structure of electric rickshaw (see Fig. 1). Due to this, often children and female passengers are more vulnerable to accidents. The improper height to breadth ratio makes the electric rickshaw more prone to toppling on undulating surfaces. Users are also feared of rear-end collision as the vehicle does not have any protection from the rear end.

Table 7 Continuation of an electric rickshaw in its present form from the safety perspective. Variable

β

t

Prob.

Gender (Female) Occupation Service/Job/Business/Self-Employed Student Others Car ownership (yes) A10 (1 = Agree/Strongly Agree) A1(1 = Agree/Strongly Agree) A6(1 = Agree/Strongly Agree) A3(1 = Agree/Strongly Agree) Constant Number of Observation = 388 Pseudo R2 = 0.3579 Log likelihood = − 155.1035

−0.757

−2.16

0.031

~ 0.200 0.734 −0.473 −1.537 −2.669 −2.023 −2.274 6.734

0.54 1.69 −1.46 −1.90 −6.45 −4.65 −5.24 6.44

0.587 0.090 0.145 0.058 0.000 0.000 0.000 0.000

electric rickshaw, as implied by the negative coefficient on gender. This is a policy-relevant finding and has wide implications for future planning and promotion of electric rickshaws. Interestingly, students will stay with electric rickshaw, all else being equal. As intuition suggests, individuals of car-owning families are less likely to continue the use of electric rickshaws. Interestingly, all relevant safety-related attributes identified in Section 4.3.1 (Table 6) are critical to the continuation of

4.4. Continuation of the use of electric rickshaw in the future Finally, a statistical model has been estimated to explore the factors shaping the continuation of the use of electric rickshaw in the future. A logistic regression model was estimated with the dependent variable as “continue = 1.” Table 7 summarizes the model estimation results. It can be concluded that females are less likely to continue the use of 7

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the use of electric rickshaws. Among all attributes, individuals are profoundly worried about protection in the form of railings and solid covers as well as the rigidity of the body of electric rickshaws. Those who opine that electric rickshaws require rear-end protection are also less likely to use the mode in the future.

carrying big shopping bags. These observations point to the changes required in vehicle designs for improving space and safety. From a safety standpoint, there is an urgent need to devise standards to augment vehicle design. Finally, the study also indicates that electric rickshaw could potentially replace the travel on common three-wheeled auto-rickshaws, which run on conventional fuels. From a sustainability viewpoint, this finding is promising, and the governments could develop policies to shift three-wheeled auto-rickshaws to electric rickshaw so that the broad goals of urban transport sustainability are achieved.

5. Summary and conclusions This research, first of its kind in the literature, explored the user profile and travel characteristics of electric rickshaw users in a proposed smart city, Patna, in India. The research employed a primary questionnaire survey and data gathered from 388 users are utilized for the exploratory analysis. The paper considered the descriptive analysis and statistical models to unravel the socio-demographics, travel characteristics and safety perceptions of electric rickshaw users. Main findings of the study are:

7. Future scope The research identifies certain avenues in the areas of electric rickshaws which could be strengthened. It would be interesting to study the travel patterns of electric rickshaw users in different categories of Indian cities for developing central-level policies to augment electric rickshaw services. There is no present research or market study that shows inequality in accessing electric rickshaws in Patna. The present study suggests that individuals of low-income households hold a low share in the sample of riders. This might be due to the higher cost of electric rickshaws in comparison to mini buses and cycles, which are common modes of the poor. This might be pointing to inequality in accessing electric rickshaw. However, further research is needed to understand inequalities related to electric rickshaw services. Further, a stated preference study with including cost and other level-of-service measures would provide better insights into the mode preferences of electric rickshaw users. Moreover, the dataset of this case study includes only current electric rickshaw users. Understanding the opinions and perceptions of non-users/potential users and comparing that with current users would provide more insights into planning and policy. This is an avenue for further research. A modeling study of future use of electric rickshaws using safety-related latent factors using a structural equations framework can also be conducted to better understand the correlation between travel behavior, attitudes and socio-demographics. Further, there also remains scope for future research to investigate why the riders would have considered auto-rickshaws as their alternative in the absence of electric rickshaws. This association may be due to the flexibility of auto-rickshaws (similar size and characteristics as of electric rickshaws). Like electric rickshaw, an auto-rickshaw can offer ‘door-to-door’ service – taking a rider from ‘door’ of origin location and drop at the ‘door’ of the destination. However, further research is needed to understand the user behavior related to the choices.

i. The proportion of males and females using electric rickshaw are nearly the same and individuals of age 25 years and above comprise a significant share of the riders. ii. The electric rickshaw is primarily used as a main mode of travel and it caters to the shopping, and educational trips. iii. Students travel more frequently and are more likely to continue the use of electric rickshaws. iv. In the absence of electric rickshaws, a majority of the riders would have used conventional fuel-based auto-rickshaws. v. Safety-related attributes such as the need for solid covers and railings and rear end protection affect the continuation of the use of electric rickshaw in the future. The research sheds light on the context of electric rickshaw use in a medium-sized city in India. From a sustainability point of view, it is a good indication that electric rickshaw is replacing the majority of trips from common auto-rickshaws because, in most of the Indian cities, auto-rickshaws often run on adulterated fuels (Gawande and Kaware, 2013), and cause more harm to the environment. The results of the research provided essential inputs to the planners and policy makers to develop policies and standards to ensure effective use of electric rickshaws. 6. Policy implications of the analysis From a broad viewpoint, the research provides valuable inputs to transportation planning and policies aimed at the promotion of electric rickshaws in Indian cities. As implied by the study, students make more trips (in a week) on electric rickshaws and are more likely to continue the use of electric rickshaws. From a policy standpoint, these observations are relevant, as students hold a significant share of (peak-period) trip makers in many Indian cities and electric rickshaw use for school/ college travel could potentially reduce peak-period emissions. It is a good indication that the “young generation” (which is the future) is more willing to use cleaner technologies. The use of electric rickshaws by students indicates an opportunity though it may be due to financial or other accessibility-related constraints. Since the younger is more acquainted with green and sustainable transport mode, this correlation is a promising finding for future sustainable transport planning. This implies that we have to make arrangements for long-distance travel (charging points at optimal locations, fast-charging batteries) within a city. Broadly, the enhancement of routes and operating schedules in relation to school/college travel, etc. could be considered to promote and sustain the use of electric rickshaws among students. Females are less likely to continue the use of the mode, as implied by the research. The current structure of the vehicle may not be female-friendly – open from all sides without doors and side rails. Females may not perceive such structure to be safe. Moreover, women undertake major household-related shopping, and the vehicle might not be conducive to

Appendix A. Grey relation analysis The following are the important steps of GRA to identify the important factors (Hsu et al., 2010; Onyegiri and Oke, 2017): (1) Construct reference data series a0:

a0 = (x 01, x 02 , ..., x 0r ) (a) where r denotes the number of respondents. The reference data series, in general, consists of r values signifying the most favored responses. (2) Construct comparison data series ai:

ai = (x i1, xi2 , ...,x ir ) (b) where i = 1,…, p stands for the number of scale items. There should be p comparison data series, and each comparison data series comprise of r values. (3) Determine difference data series Δi: i

= (|x 01

x i1|, |x 02

x i2|, ...,|x 0r

x ir |) (c)

(4) Obtain Global Maximum Value Δmax and Global Minimum Value 8

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Δmin in the difference data series Δi: max

= max (max i ) i

min

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= min (min i ) (d) i

(5) Convert each data point in each difference data series to a grey relation coefficient θi (f): max = min (e) where Δi(f) is the f value in Δi difference data i (f) + max series and coefficient ω is defined between 0 and 1. Usually, the value of ω is taken as 0.5 for easy computation of grey relation coefficient θi (f) (Kumar and Bhattacharyya, 2017).

+

i (f )

(6) Obtain grey relation grade for each difference data series: r

i = r n= 1 i (n) (f) where Γi represents grey relation grade for the ith scale item, and assume that data points in the series are of the same weights. 1

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