Passengers’ perceptions of safety in paratransit in the context of three-wheeled electric rickshaws in urban India

Safety Science 124 (2020) 104591

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Passengers’ perceptions of safety in paratransit in the context of threewheeled electric rickshaws in urban India Shiv Priye, M. Manoj

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

A R T I C LE I N FO

A B S T R A C T

Keywords: Electric rickshaws Paratransit Occupants safety Safety perceptions Patna

The electric rickshaw is a battery-powered three-wheeled paratransit that has gained popularity in urban India due to its flexible service and affordability. It is an environment-friendly and energy-efficient paratransit mode of transport. However, there remain serious concerns about the safety of its occupants in the heterogeneous traffic conditions prevailing in India. This paper presents a preliminary study to understand passengers’ perceptions of electric rickshaw safety in Patna, India. Such a study is important to design safer vehicles, promote safe driving behaviours, and create a safer driving environment. The study applies descriptive and econometric analyses to investigate the safety perceptions of 388 participants. The results reveal that there are two latent constructs associated with passengers’ attitudes towards safety, unsafe vehicle structure and unsafe vehicle dynamics. The first construct includes statements related to the structure of electric rickshaws such as an unstable body, the lack of rear-end protection, and absence of solid railings and coverings. The second construct includes statements concerning dynamic aspects of the rickshaw, such as fear of overturning and reckless driving. Further, this study finds that passengers’ perceptions of both safety constructs vary based on individual and household characteristics. The results suggest that females, older individuals, and high-income passengers are strongly dissatisfied with the unsafe structure and dynamics of the vehicle. Individuals from car-owning households display greater concern about the unsafe vehicle dynamics than those from non-car owning households,. Finally, the study estimates an empirical model to explore the overall safety perceptions of electric rickshaw riders. The model highlights that participants attach equal importance to both safety constructs and that there is no individuallevel variation regarding the overall safety assessment of passengers, as reinforced by the statistical insignificance of socio-demographic characteristics.

1. Introduction Intermediate public transport plays a significant role in developing countries, especially in African and Asian regions. This is primarily due to the lack of formal public transport or poor public transport services (Kumar et al., 2016). Intermediate public transport or paratransit, owned and operated by private parties, completes the role of public transport by serving as feeder modes, and in some regions covers the service gaps created by poor transit operations (Cervero and Golub, 2007). Most of these paratransit operations include small and low-mass motorised three-wheelers having the required manoeuvrability and operating features to travel through the narrow and crowded streets of cities. Paratransit has gained popularity among low- and medium-income groups due to its flexibility and affordability (Sindha et al., 2018). The presence of paratransit in developing countries such as India indicates its role in ensuring mobility for the population (Maitra and

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Sadhukhan, 2013). It also generates urban employment, especially for the unskilled workforce. Given the emphasis on sustainable mobility and accessibility in the Indian government’s ‘Smart Cities Mission’, it could be said that intermediate public transport will play an important role in the years to come (Smart Cities Mission, 2018). However, despite their significant role in urban contexts, most of these motorised three-wheelers have attracted negative perceptions due to their contributions to noise and air pollution and traffic congestion, as well as their involvement in fatal crashes (Mohan et al., 1997; Mohan and Tiwari, 2000; Harding et al., 2016; MoRTH, 2017). Globally, the riders of three-wheelers are among the most vulnerable categories of road fatalities because they are less protected and the chances of being involved in crashes, injuries, and fatalities are high (Jayatilleke et al., 2015). According to the WHO (2018), in the South-East Asian and Western Pacific regions, the majority of road fatalities are among the users of motorised two-wheelers and three-wheelers. In the

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

https://doi.org/10.1016/j.ssci.2019.104591 Received 11 April 2019; Received in revised form 1 November 2019; Accepted 22 December 2019 0925-7535/ © 2020 Elsevier Ltd. All rights reserved.

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Fig. 1. Electric rickshaws in India.

have indicated that consumer evaluation of safety performances can influence vehicle purchase decisions (Koppel et al., 2008) and mode choice (Delbosc and Currie, 2012; Shiwakoti et al., 2019). Delbosc and Currie (2012) conclude that although personal safety is directly related to transit ridership, it has not been extensively studied in the literature. Several studies related to overall service performance assessment highlight the importance of passengers’ safety in public transportation (Lu and Tseng, 2012; Márquez et al., 2014; Suman et al., 2016). The Transportation Research Board considers safety and security decisive factors in measuring the performance of public transit (TRB, 2003). According to Joewono and Kubota (2006) and Munira et al. (2013), much less importance is given to the safety and security criteria in the case of service quality evaluation of public transport in developing countries, and the situation is even worse in the case of paratransit. Studies related to the safety analysis of private vehicles such as cars and bikes are available, but little has been done for three-wheelers, which are the preferred mode of travel for medium- and lower-income individuals of developing countries (Mohan, 2002).

heterogeneous traffic conditions prevailing in these regions, paratransit vehicles are subject to a variety of vehicles and driving situations, motorist and pedestrian behaviours, different weather conditions, and lighting and road surface conditions (Hulse et al., 2018). Road fatalities are in fact very high in developing countries irrespective of the population and the number of motor vehicles in use (WHO, 2018). This might reflect the lack of infrastructure planning and improper vehicle safety features and designs. Also, there are differences between the patterns of road traffic and road traffic injuries in developed and developing countries (Mohan, 2002). Human behaviours such as travelling at higher average speed, consumption of intoxicating substances, not wearing protective devices, and the use of mobile phones while driving are also associated with road traffic crashes and injuries (Petridou and Moustaki, 2000; WHO, 2016). The majority of the scientific research on road traffic crashes has been carried out in developed countries, especially regarding car occupants. However, research on the safety of small low-mass vehicles, particularly three-wheelers, is limited (Chawla et al., 2003). Low-mass vehicles and in particular low-mass electric vehicles are generally not designed to withstand crashes and collisions (Egertz, 2011). The specific issues of small and low-mass electric vehicles are reduced front length, lower mass than other vehicles, and heavy batteries (Kaeser et al., 1994). In one study, Schmucker et al. (2011) highlighted the restricted crashworthiness and severe injury risk of low-mass motorised three-wheelers, even at low crash speeds. Three-wheelers are considered unsafe due to their unstable and fragile structure, open sides, and absence of safety devices such as seat belts and air-bags (Vasiljevic et al., 2012; Vadysinghe et al., 2018). Poor roll-over stability is one of the drawbacks of three-wheelers which make them vulnerable to road crashes (Huston et al., 1982; Sindha et al., 2018). Road traffic in developing countries comprises a large proportion of vulnerable road users; therefore, vehicles and infrastructure should be designed with due consideration to safety (Mohan, 2002). For decades, scientists and transportation planners have been attempting to make the vehicle environment safe and secure for users (Winston and Mannering, 1983; McCarthy, 1990). Urban transport faces challenges in ensuring the safety and security of travellers and non-travellers in an urban space. International literature related to public transport recognises the importance of the safety and security of its users (Rundmo et al., 2011; Schmucker et al., 2011; Daziano, 2012). According to Márquez (2016), perceptions of safety are highly complex and depend on various factors, including the city and its environment, user characteristics, and the mode of travel. Indeed, safety and security are the factors that drive the intention to use a particular mode of transportation (Koppel et al., 2008; Nordfjærn et al., 2015). Studies

1.1. Three-wheeled electric rickshaws in India Since the world is dealing with the serious issues of energy security and greenhouse gas emissions, researchers and planners are looking for sustainable transport alternatives (Posada et al., 2011). Electric vehicles are touted as an energy-efficient mobility option, particularly in urban areas where a large number of conventional-fuel vehicles can be seen (Solero et al., 2001; Caricchi et al., 2003). Recently, battery-powered, three-wheeled electric vehicles (popularly known as electric rickshaws) have entered Indian markets (see Fig. 1). In short order the market shares of these ‘low-mass’ electric three-wheelers have grown exponentially in the urban agglomerations of India (Harding and Kandlikar, 2017). With one front wheel and two rear wheels, electric rickshaws have a maximum seating capacity of five persons, including the driver. 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 4000 W, to carry passengers for hire’ (Motor Vehicles (Amendment) Act, 2015). By relying on brushless DC motors and lead-acid batteries, such vehicles ensure zero tail-pipe emissions (Majumdar and Jash, 2015). Electric rickshaws are a cost-effective transport option in many cities of India, where there is a public transport deficit. These vehicles came out at a time of policy-level discussions on energy security and greenhouse gas emissions and are regarded as affordable and environment-friendly modes which have the potential to reduce the carbon footprint of passenger transport activities, thereby ensuring competition for other 2

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concludes the paper and highlights the implication of the study findings for planning and policy. The final section discusses the limitation of the study and identifies possibilities for future studies.

popular paratransit modes, such as conventional (gasoline-based) threewheeled auto-rickshaws and mini-vans. 1.2. Safety concerns for three-wheeled electric rickshaws

2. Method Their availability, affordability, and environment-friendliness have increased the popularity of electric rickshaws (Singh, 2014). However, there remain serious concerns about the safety of the occupants when these vehicles operate in the mixed traffic corridors of Indian cities. The body of an electric rickshaw is much lighter than that of a conventional fuel-based three-wheeled auto-rickshaw. The structure is very fragile, unstable, and prone to topple. It is open on sides and does not have any safety devices such as safety belts or anti-lock braking systems (ABS). Unlike cars, electric rickshaws have not been designed for crashworthiness in collisions (Sindha et al., 2015). Riders have opined about a sense of fear while travelling in electric rickshaws (Bhasin and Bhardwaj, 2014), but hardly any studies of public experiences exist in the academic literature. A recent report on road crashes by the Government of India reveals an increasing trend of fatal crashes involving electric rickshaws. In 2016, nearly 380 (registered cases of) fatal crashes occurred in Indian cities in which electric rickshaws were involved (MoRTH, 2017). Because of these issues, many state agencies are planning to ban electric rickshaws from transporting children to schools (e.g., Government of Bihar, 2016). Therefore, safety remains a serious concern regarding electric rickshaw use for passenger transport unless appropriate countermeasures are implemented. Initially, electric rickshaws were not legalised, as they were not covered in the Indian Motor Vehicle Act, 1998. However, recent government interventions mandate that electric rickshaws be registered within the respective city’s administrative limits. Recent legalisations have set the speed limit for electric rickshaws at 25 km/hr and have prompted the requirement of licenses to operate electric rickshaws (Motor Vehicles (Amendment) Act, 2015). However, Goswami et al. (2018) have noted a lack of awareness among electric rickshaw drivers about the maximum permissible speed limit. The speed limit has implications for the safety of electric rickshaw riders since the speed maintained by other vehicles in mixed traffic streams could be high, implying the vulnerability of electric rickshaw users in case of crashes, as indicated by Mohan et al. (1997). Furthermore, the low speed of electric rickshaws could also hamper the flow and emission characteristics of mixed traffic streams (Majumdar and Jash, 2015) and worsen traffic situations in cities (CapaCITIES, 2018). Since most parts of electric rickshaws are manufactured in local workshops, there remain concerns about strict adherence to safety standards in their design and manufacture (Padmanabhan et al., 2014). In recent years, many Indian cities have seen a proliferation of electric rickshaw services. Market research reports have documented issues related to risky driving and traffic rules violations by electric rickshaw drivers (CapaCITIES, 2018). However, passenger perceptions of these factors, variations in safety perceptions, and their part in the general safety assessment of electric rickshaws are unknown. It is essential to know passengers’ safety preferences in order to design safer vehicles, encourage safe driving behaviour, and create a safer driving atmosphere. Effective safety-related policies could be developed if we understand the safety perceptions of passengers (Daziano, 2012). With this brief background of electric rickshaws in India, the present study aims to explore passengers’ attitudes toward different safety attributes of electric rickshaws and the role of such attitudes in their overall safety perceptions. In addition, the paper investigates variations in safety perceptions across different socio-demographic segments. With these objectives, the research is presented as follows. The next section introduces the study method. Section 3 presents the results, a descriptive summary of the data followed by a factor analysis performed on attitudinal variables. A segmented analysis of safety perceptions and an empirical model to assess the overall safety perceptions of users are also described. Section 4 discusses the results and Section 5

2.1. Study area This study is based on a data set collected from a medium-sized city, Patna, in India. Under the Government of India’s ‘Smart Cities Mission’ (Smart Cities Mission, 2018), Patna was chosen as a ‘Smart City’. The promotion of sustainable and clean transportation technology is one of the highlights of the Smart Cities Mission. The population of Patna Municipal Corporation (PMC) is about 1.68 million (Census of India, 2011). The PMC region has an area of 99.45 sq. km and the road density is 13 km per sq. km. The road network is insufficient, as only 10% of the area is available for traffic flow against the standard requirement of 15–20% (Government of Bihar, 2011). The formal public transport service in Patna is inadequate (Sinha et al., 2017), which has led to the proliferation of intermediate public transport, with a modal share of 27% (TERI, 2014). The services given by electric rickshaw in Patna are similar to those of common paratransit modes. This means that electric rickshaws do not adhere to fixed routes and timetables and have no specified stops. Electric rickshaws follow ‘demand-responsive’ operations in Patna. 2.2. Survey design A questionnaire-based survey approach was applied, which is widely used in research related to behaviours, attitudes, and perceptions (Shiwakoti et al., 2016). The questionnaire was printed on paper and there were no electronic information collection tools involved. The questionnaire comprised three sections. In the first section, statements related to attitudes and perceptions toward safer riding in the electric rickshaws were scored on a five-point Likert-type scale where 1 indicated strongly disagree and 5 indicated strongly agree. In addition, one question asked respondents to rate their level of agreement with the ‘overall safety’ of the electric rickshaw on the same scale. The Likerttype scale (Likert, 1932) is the most fundamental and frequently used psychometric scale for measuring a respondent’s perceptions. The scale also enables us to perform statistical analyses (Klein and Kantor, 2018). The second and third sections concerned information relating to the socio-economic and travel characteristics of users, respectively. Travel behaviour details (concerning recently completed trips) are not included in the present study. The statements related to the first segment and their codes are described in Table 1. For the selection of statements related to safety perceptions and Table 1 Statements related to safety perceptions and attitudes (with their codes). Codes

Statements

A1 A2

The body of electric rickshaw shakes while driving on undulating roads Electric rickshaw will have a severe impact in case of crashes due to its very light body Electric rickshaw must have solid cover and railing 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

A3 A4 A5 A6 A7 A8 A9 A10 A11 A12

3

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2.4. Data analysis

attitudes of electric rickshaws, a thorough literature review was performed. Existing literature on light motor vehicles have described toppling, operating speed, the structure and body of the vehicle, safety features, riding behaviour, drivers’ skills, pavement condition, traffic conditions, and infrastructure as some of the critical aspects related to safety (e.g., Huston et al., 1982; Delisle et al., 1988; Dharmaratne and Stevenson, 2006; Schmucker et al., 2011; Rana et al., 2013; de Silva et al., 2014; Goswami et al., 2018; Sindha et al., 2018). In addition, a number of discussions with manufacturers and professionals in the field of transport safety were also carried out to understand the ergonomics of the electric rickshaw. About 20 commuters belonging to distinct age groups were also surveyed in this respect to learn about their experiences while travelling on electric rickshaws. Several newspaper reports were also studied in connection with crashes involving electrical rickshaws to observe the reasons for the crashes. Subsequently, a pilot study was performed with 40 electric rickshaw users in Patna incorporating all relevant attributes to identify the potential issues with the survey tool. The final survey tool was redesigned with minor changes required as per feedback from the pilot study. This survey was designed particularly for the passengers of electric rickshaws, who responded based on their general perceptions and experiences of electric rickshaws so as to prevent self-selection bias in the return (Currie et al., 2013). Furthermore, the interviewers were trained to capture responses from illiterate persons. Prior to the survey, a detailed report was submitted to the authors’ institute describing the scope of the study, study methods, source of funding, data requirement, target population, etc. The survey was implemented after receiving the required permissions from the competent authorities of the institute.

The study incorporates exploratory data analysis and statistical modelling to investigate safety perceptions. First, we conducted a descriptive analysis of the survey data related to the socio-demographics of respondents and their levels of agreement with several measurement questions related to underlying safety factors. Furthermore, to measure the internal consistency of the responses, a statistical reliability test using Cronbach’s alpha coefficient was conducted on the measured attributes. Second, in order to understand the latent safety constructs associated with the measured variables and to reduce variable dimensions, Exploratory Factor Analysis (EFA) was performed on the measured variables. Principal Component Analysis (PCA) was conducted on the qualitative variables to understand the correlation pattern among the measured variables. The factors or the number of dimensions was extracted by keeping a cut-off eigenvalue of 1 and a factor loading of 0.50 (Hair et al., 2006). Third, Confirmatory Factor Analysis (CFA) was performed on the measured variables to test whether the factors revealed in EFA conformed to the factor structure. Although the sample of 388 users of electric rickshaw was small, it was statistically acceptable, as mentioned in Section 2.3. In the past, many similar studies related to travel behaviour have conducted complex econometric modelling with similar-sized or smaller samples (e.g. Chen, 2008; Lu and Tseng, 2012; Şimşekoğlu et al., 2013; Márquez et al., 2014; Deb and Ahmed, 2018; Simsekoglu and Nayum, 2019). Fourth, a segmented analysis of safety performance was conducted to ascertain any significant variation among socio-demographic groups regarding their perceptions of the safety of electric rickshaws. Finally, an ordinal logistic regression model was employed to investigate the relationship between overall safety perceptions and socio-demographics and latent factors. An ordinal logistic regression model was appropriate because the dependent variable (code AA, Table 3) was scored on a 5-point Likert type scale (an ordinal scale), and these models yield estimates which are consistent and efficient (Rifaat et al., 2012; Shiwakoti et al., 2016). As explained by Amemiya (1985), an unordered model (such as the multinomial logit model) would otherwise affect the efficiency of parameter estimates.

2.3. Data collection The survey was conducted during March–April 2018 using a simple random sampling technique in which each interviewee had an equal chance of being selected for the survey. The survey was voluntary, without particular attention to the identity and personal data of the respondents. In this survey, the participants did not see the survey instruments, nor did they record the responses on the survey questionnaires. Trained volunteers were employed to conduct face-to-face interviews. The volunteers asked the survey questions in the language of choice of the respondents and marked the responses on the survey sheets. Attention was paid to carefully describing the questions and response fields to the respondents. Moreover, the questions asked were simple in nature and did not have any technical terms so as to avoid ambiguity. It was a kind of intercept survey (e.g., Cherry and Cervero, 2007) in which the electric rickshaw users were approached (i) on board randomly selected electric rickshaws and (ii) in PMC regions with frequent electric rickshaw services. For the study, 560 users of electric rickshaw were approached. However, only 410 users actually participated (73.21% response rate). It was discovered that there were some missing or multiple responses on 22 questionnaire forms, which were then removed, and 388 completely filled questionnaires were obtained after refinement. Since the population of electric rickshaw riders is not defined, a sample of 388 participants was found to be statistically acceptable according to Cochran’s formula (Cochran, 1977) by assuming maximum variability and taking a confidence level of 95% with ± 5% accuracy. Assuming a confidence level of 95% and ± 5% precision, the required sample size is 382. Hence, a sample size of 388 participants was eventually adopted for the study. Additionally, the sample representativeness was assessed through a one-sample t-test of the gender distribution of the general population. The proportion of males in the general population (Patna) is 0.53 (Census of India, 2011). The null hypothesis that there is no significant difference between the sample and the population was retained at the 5% significance level [tvalue = 0.6].

3. Results 3.1. Summary of Socio-demographic characteristics This section presents an overview of the socio-economic profile of electric rickshaw riders in Patna. Table 2 highlights the descriptive statistics of the socio-demographic variables collected in the study. It can be seen that the proportions of both genders are approximately equal, with the Bernoulli random variable (Male = 1) assuming a mean value of 0.5155. Interestingly, electric rickshaw riders are more likely to belong to the middle-age category (35–55 years) with skewness towards the lower end of the age brackets. The respondents are more likely to be graduates, as implied by the trends in completed education status. The average household size of an electric rickshaw rider has more than four members, as suggested by the household size distribution. Interestingly, students, followed by housewives/retired individuals (‘others’), are more likely to use electric rickshaws. The proportion of student riders is promising since the future use of sustainable modes would be related to habit formation when young. Surprisingly, individuals of medium- and high-income households (income > INR 30,000) are more likely to use electric rickshaws, as implied by the income distribution. This association may reflect poor levels of service offered by other modes in the residential locations of medium- and high-income households. The household vehicle ownership distribution suggests that nearly 83% of respondents have at least one motorised vehicle at their home. While this distribution seems to be on the higher side given the context of electric rickshaw use, further research is needed to explore the association between vehicle ownership levels and 4

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agree). The descriptive statistics suggest that the riders feel the rickshaw framework to be light, unstable on undulating roads, unsafe for small children, and requiring a solid cover and railings and rear-end protection. However, the respondents are nearly indistinguishable regarding their levels of agreement with the statements related to seat belts and safety during night travel. The last row of the table presents the descriptive summary of the responses to the question related to the overall safety perceptions of riders. A further statistical reliability test was performed using Cronbach’s alpha coefficient on the responses linked to safety attributes to assess the internal consistency of the responses. The estimated Cronbach’s alpha coefficient of 0.864 (George and Mallery, 2003) indicated that the questionnaire could meaningfully measure all safety attributes, and that there were significant differences among the ratings (collected from electric rickshaw users) for safety attributes. In order to understand the latent safety constructs associated with the measured variables (A1–A12) and to reduce variable dimensions, factor analysis was performed on the measured variables. The next section discusses the results of the analysis.

Table 2 Socio-economic characteristics of electric rickshaw users. Socio-economic characteristics

Classification

Frequency

Percentage

Gender

Male Female < 18 18–24 25–34 35–55 > 55 Illiterate Primary School Matriculation Intermediate Graduation PG and above Service/Job Business Student Self-employed Others Up to 10,000

200 188 50 60 88 136 54 8 26 42 136 146 30 90 26 128 26 118 18

51.55 48.45 12.89 15.46 22.68 35.05 13.92 02.06 06.70 10.82 35.05 37.63 07.73 23.20 06.70 32.99 06.70 30.41 04.64

10,000–20,000 21,000–30,000 31,000–40,000 41,000–50,000 More than 50,000 Car Motorized Two-Wheeler (MTW) Bicycle None 1 2 3 4 5 6 and above

44 60 80 96 90 152 172

11.34 15.46 20.62 24.74 23.20 39.18 44.33

75 87 46 52 70 136 52 32

19.33 22.42 11.86 13.40 18.04 35.05 13.40 08.25

Age

Education

Occupation

Household Monthly Income (INR*)

Vehicle Ownership

Household Size

3.3. Exploratory factor analysis To identify the correlation pattern among the measured variables, Principal Component Analysis (PCA) was conducted among the qualitative variables. The results of PCA are shown in Table 4, suggesting that the key model selection indicators are within the accepted ranges. The Kaiser-Meyer-Olkin (KMO) coefficient value is 0.863, indicating that sampling is adequate and that the data support factor analysis (Kaiser, 1974; Cerny and Kaiser, 1977). Furthermore, the Bartlett’s test of sphericity reported a p-value of 0.001, which strengthens the view that the data could be used for structure detection (Snedecor and Cochran, 1989). The factors in Table 4 were extracted by keeping a cutoff eigenvalue of 1 and a coefficient magnitude of 0.50. The PCA yielded two latent factors, which together explained nearly 55% of the variance in the measured variables. The variance was found to fall in the variance ranges reported in several social science papers and travel behaviour studies (Williamson et al., 1997; Peterson, 2000; Nordfjærn et al., 2014). The Cronbach’s alpha coefficients estimated for Factor 1 and Factor 2 are 0.76 and 0.79, respectively. These estimates further suggest that the measures have high internal consistency and pertain to the respective groups (Cortina, 1993). Two measured items—the threat of snatching and ride quality (Table 3)—did not appear relevant for the constructs derived through the analysis. The factors can be broadly viewed as arising from the ‘static’ and ‘dynamic’ issues of electric rickshaws. Factor 1 measures the static characteristics of the vehicles—those measures related to the body of the rickshaws are attached to this factor. The statements encompassed by the construct include those related to the light body; requirements for solid rails and

* INR = Indian Rupees.

electric rickshaw use. 3.2. Overview of safety indicators In order to explore the latent safety perceptions of the riders, several attitudinal questions were asked in the survey. Table 3 lists the attributes and the relevant descriptive statics. The attributes were finalised after consultation with vehicle safety experts from the authors’ institute. The measured variables were mainly related to safety ‘within’ and ‘outside’ the vehicle, and the responses against them were captured on a five-point Likert-type scale (1 = strongly disagree to 5 = strongly Table 3 Description of safety attributes with their abbreviations. Codes

Attributes

Mean*

Std. Dev.

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

The body of electric rickshaw shakes while driving on undulating roads Electric rickshaw will have a severe impact in case of crashes due to its very light body Electric rickshaw must have solid cover and railing 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 travelling alone Electric rickshaw is unsafe in a mixed-traffic situation Travelling in an electric rickshaw during night time is unsafe Overall Safety

4.106 4.582 4.265 3.518 3.781 4.113 3.778 3.606 2.966 4.490 3.794 2.887 2.363

0.849 0.504 0.819 1.126 0.820 0.665 1.013 1.124 1.225 0.620 0.718 1.229 1.120

* Likert-type five-point ordinal scale was used (1 = strongly disagree to 5 = strongly agree). 5

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respondents about the respective safety feature.

Table 4 Principal component analysis of measured safety attributes. Codes

A2 A3 A6 A10 A11

A4 A5 A7 A9 A12

Attributes

Factor 1

3.4. Confirmatory factor analysis

Factor 2

Factor-1: Unsafe Structural Design (Total Variance Explained: 41.63%; α: 0.76) Electric rickshaw will have a severe impact in case 0.695 0.145 of crashes due to its very light body Electric rickshaw must have solid cover and railing 0.775 0.137 There should be protection from rear-end collision 0.585 0.125 Electric rickshaw is unsafe for small children who 0.715 0.285 are travelling alone Electric rickshaw is unsafe in a mixed-traffic 0.650 0.302 situation Factor-2: Unsafe Vehicle Dynamics (Total Variance Explained: 12.55%; α: 0.79) Drivers drive recklessly and violate traffic rules 0.022 0.804 Fear of over-turning comes when electric rickshaw 0.398 0.608 takes a sharp turn at curves There is a feeling of unsafe when high-speed 0.329 0.593 vehicles pass electric rickshaw very closely There must have seat belts for every rider 0.195 0.718 Travelling in an electric rickshaw during night time 0.233 0.767 is unsafe

In order to test whether the factors revealed in Section 3.3 conform to the factor structure, a confirmatory factor analysis was performed on the measured variables. The analysis suggests that a two-factor model could explain much of the variability in the data. The model fit indices were: χ2 = 226.93, with df (degree of freedom) = 35 (χ2/df = 6.48); RMSEA (root mean squared error of approximation) = 0.119; CFI (comparative fit index) = 0.830; and TLI (Tucker-Lewis index) = 0.782. Models having χ2/df values less than 5 (Lai and Chen, 2011), RMSEA values less than 0.08 (Hair et al., 2006), CFI values greater than 0.9 (Hooper et al., 2008), and TLI values greater than 0.9 (Joewono and Kubota, 2007) indicate a good fit. Therefore, the fit statistics suggested that improvements were needed in the model. We investigated the possibility of correlation patterns among the measured variables by taking one at a time, and several models were estimated. The data suggested the existence of a statistically significant correlation between the latent factors that was considered in the final model. The addition of the correlation (between-factors) coefficient improved the model fit statistics: χ2 = 84.870 with df = 34 (χ2/df = 2.49). The other fit indices are RMSEA = 0.062; CFI = 0.955; and TLI = 0.940. These values show that the final model is statistically better than the baseline model. Hence, the model allowing for correlation between the latent constructs was retained for further analysis and discussion. The structural model is presented in Fig. 2. The standardised coefficient estimates are also reported in the figure.

covering; and protection in case of rear-end collision. The statements related to child safety and safety in mixed traffic conditions are also attached to Factor 1. This could reflect the fact that the passengers are exposed to passing traffic in electric rickshaws due to the lack of a closed body (see Fig. 1). Factor 2 is distinct from Factor 1 since there are no items in Factor 2 that are present in Factor 1. In other words, there is no cross-loading issue. Factor 2 mainly attaches to measured variables that are related to the ‘dynamic’ aspect of the vehicles. The factor gathers statements related to movement aspects such as reckless driving, fear of overturning, the requirement for seat belts, and the presence of high-speed vehicles in the traffic stream. Since the last statement (A12) is associated with Factor 2, it may be interpreted that due to the high-speed mixed-traffic conditions, riding electric rickshaws is unsafe during night. As electric rickshaws are unique to Indian cities and there has been little research on their safety aspects, identifying equivalent factors from prior research is impossible at this stage of the study. The latent factors are named ‘unsafe structural design’ (Factor 1) and ‘unsafe vehicle dynamics’ (Factor 2). The interpretation of the latent factors is straightforward given the construction of measurement questions. The higher the score on each of the factors, the greater the concern of the

3.5. Segmented analysis of safety perception The objective of this section is to ascertain whether there are significant variations among the socio-demographic groups regarding the perceptions related to the safety of electric rickshaws. Table 5 presents the results of the hypothesis tests regarding average scores. The null hypothesis that group means are equal was evaluated using non-parametric tests, namely the Mann-Whitney U test and Kruskal-Wallis H test, as dictated by socio-demographic groups. The reason for choosing non-parametric tests is that they are distribution-free. Morton et al. (2016) employed a similar methodology in a study of service quality assessments of bus transit in Scotland. The Mann-Whitney U test is

Fig. 2. Confirmatory factor analysis of the final model. 6

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positive scores on both latent factors, implying their agreements with both safety issues. While those who report their job type as ‘business’ showed negative scores, individuals of other job categories displayed mixed evaluations of both safety factors. With regard to monthly household income, the variations observed across the defined income categories are significant. It is interesting to see that individuals of lowincome bands (up to INR 20,000) showed negative scores on both factors, implying that they are less concerned about the latent safety features. Respondents whose monthly income is above INR 40,000 are more concerned about the safety issues pertaining to the structural design and vehicle dynamics, whereas, the individuals of the middleincome range display different attitudes regarding the two latent factors. It may be concluded that such respondents have heightened concerns about the unsafe structural design of electric rickshaws, but tend to show less concern regarding unsafe vehicle dynamics. Finally, the effect of car ownership showed no significant variation for Factor 1. However, individuals from car-owning households display greater concern about the unsafe vehicle dynamics than those from non-car owning households.

Table 5 Perceived safety of latent parameters across socio-economic groups. Variable

1

Gender Male Female Age (years)2 < 18 18–24 25–34 35–55 > 55 Education Status2 Illiterate Primary school Matriculation Intermediate Graduation PG and above Occupation2 Service/Job Business Student Self-employed Others Household Monthly Income2 Up to 10,000 10,000–20,000 21,000–30,000 31,000–40,000 41,000–50,000 More than 50,000 Car Ownership1 Yes No 1 2

Unsafe Structural Design

Unsafe Vehicle Dynamics

Mean

Mean

p-value:0.000 −0.254 0.270 p-value:0.014 −0.412 0.116 −0.013 0.005 0.261 p-value:0.000 −1.437 −0.229 −0.475 0.090 0.139 0.158 p-value:0.001 0.077 −0.518 −0.169 0.037 0.231 p-value:0.000 −1.255 −0.608 0.102 0.223 0.189 0.081 p-value:0.768 −0.020 0.013

SD

1.093 0.810 1.140 1.171 0.935 0.916 0.869 0.194 1.214 1.074 0.913 0.961 0.912 0.889 0.801 1.126 1.303 0.825

0.705 0.918 1.023 0.889 0.960 0.933 0.982 1.013

p-value:0.000 −0.442 0.470 p-value:0.000 0.567 0.122 −0.186 −0.261 0.299 p-value:0.000 −0.977 0.422 0.344 −0.074 −0.190 0.676 p-value:0.000 −0.298 −0.828 0.247 −0.046 0.152 p-value:0.000 −1.030 −0.774 −0.376 −0.149 0.119 0.841 p-value:0.000 0.797 −0.513

SD

0.858 0.924 0.741 0.915 0.802 1.104 1.030 0.162 0.743 0.920 0.928 1.098 0.592 1.000 1.194 0.908 0.845 0.940

3.6. Empirical model for overall safety perceptions The objective of this section is to ascertain the impacts of the perceived safety constructs (Factor 1 and Factor 2) on overall safety perceptions of electric rickshaw riders. Since the dependent variable is measured on an ordinal scale (code AA, Table 3), an ordinal logistic regression model was employed to investigate the relationship between overall safety perceptions and socio-demographic and latent factors (as discussed in Section 2.4). Prior to modelling the association, correlation coefficients between ‘overall safety perception’ and the latent factors were estimated. The analysis displays moderate but significant (at 5% level) correlations among the variables, with coefficients for Factors 1 and 2 on overall safety perception being −0.518 and −0.506, respectively. The significant correlations also imply that the measured variables are useful for explaining the overall safety perceptions of electric rickshaw riders. The empirical model is presented in Table 6. The modelling exercise included a step-wise approach to select variables (at 5% level). However, none of the socio-demographic variables remained significant in the model for the selected significance level of 5%. Hence, it may be concluded that the riders are homogenous (95% confidence level) in terms of their overall safety perceptions regarding electric rickshaws. What matters in their evaluation is their perceptions about the structural and dynamic safety aspects of the rickshaws. It should also be emphasised that respondents attached equal importance to both factors since the coefficients on both factors are nearly the same (Table 6). The model reports a significant χ2 value, which shows that it has some degree of explanatory power over an intercept-only model (Morton et al., 2016). The Nagelkerke pseudo-R2 value of 0.547 indicates that the model accounts for a significant portion of the variation in perceived overall safety. Overall, the fit indices suggest that the model is statistically acceptable (Morton et al., 2016; Bellizzi et al., 2018). The model findings are intuitive: individuals with higher scores on the

0.502 0.794 0.706 0.868 1.037 0.701 0.705 0.807

Mann-Whitney U Test. Kruskal-Wallis H Test.

found to be suitable when only two independent groups are taken into consideration for a categorical independent variable, and KruskalWallis H test is used for three or more independent groups (Sadhukhan et al., 2018). The positive sign on average scores represents heightened concerns/strong agreements with safety issues (refer to Table 4 for factor definitions). The statistical tests yielded interesting results. With regard to gender, attitudes towards both latent constructs vary, as implied by the p-values (both less than 0.05). It may be inferred that female riders attach higher importance to both structural and dynamic safety aspects of the vehicle. With regard to the influence of age, the hypothesis tests suggest that the responses are significantly different. Individuals of young age (age < 18 years) express heightened concerns related to the dynamic aspects of the vehicles (Factor 2), whereas they showed negative loading on the latent measure related to the structural aspect of the vehicle. However, individuals of the mid-age group (aged 25–34 years) display consistent attitudes towards both factors. A similar trend is observed among individuals of older age (age > 55 years). Individuals who are uneducated or qualified up to matriculation are observed to be less concerned with ‘structural’ safety (Factor 1), whereas those who have qualified above matriculation agree about the unsafe structural design features of electric rickshaws. This trend is reversed in the case of Factor 2. It can be seen that individuals who have received primary education or have matriculated have a greater likelihood of displaying stronger concerns about the latent ‘dynamic’ safety factor. On the other hand, individuals who are a qualified ‘intermediate’ class or educated up to matriculation show a propensity to display negative loadings on the second factor. In terms of reported job status, we note that unemployed/retired individuals (‘others’) showed

Table 6 Estimation results of ordinal logistic regression model. Variable

Beta

t-statistic

Factor 1 (Unsafe Structural Design) Factor 2 (Unsafe Vehicle Dynamics) Model Fit Indices −2LL (intercept) −2LL (final) Chi-square (df = 2) Nagelkerke-R2

−1.450 −1.404

11.69 11.32

* p-value = 0.00. 7

1071.555 791.343 280.211* 0.547

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factors ‘unsafe structural design’ and ‘unsafe vehicle dynamics’ are less likely to evaluate the electric rickshaws as a safe mode of transport.

features, reckless driving, and external factors. Structural and dynamic aspects of vehicles have been found to affect the evaluation of the safety of vehicles (McCarthy, 1990; Chang and Yeh, 2005; Morency et al., 2012; Meena et al., 2014; Sam et al., 2018). The segmented analysis reveals that perceptions about the latent constructs vary with the sociodemographic background of the respondents. It can be seen that female respondents attached higher importance to both structural and dynamic safety aspects of the vehicle. In general, female respondents express stronger concerns about the safety of public-use vehicles (Stradling et al., 2007; Verma et al., 2017), and have also been found to perceive risks to be high and give higher ratings to the risk attributes (Moen and Rundmo, 2004). Previous studies indicate that females have greater concerns about risk sources and their consequences and exhibit a higher demand for transport risk improvement (e.g. DeJoy, 1992; Nordfjærn and Rundmo, 2010; Hulse et al., 2018). It has also been observed that there is a high risk of injuries to females from entanglement of the dupatta (a traditional female attire in India) or scarf with parts of the same or another vehicle (Meena et al., 2014). It is observed that older individuals also exhibit heightened concerns about the unsafe structure and dynamics of electric rickshaws. As is evident from the literature, perceived risk (or safety perception) is higher in the case of older individuals than middle-aged and younger individuals. Koppel et al. (2008), Moen and Rundmo (2004), and Daziano (2012) have observed that older individuals give higher importance to vehicle safety aspects than other vehicle aspects (such as comfort or reliability). Along similar lines, unemployed/retired individuals seem to show heightened concern regarding the ‘unsafe structure’ factor, as observed by Bastide et al. (1989) and Davidson and Freudenburg (1996). Since the unemployed/retired cohort includes physically disadvantaged groups and homemakers of young and middle age, the perceptions could mainly be due to the absence of guardrails and crash barriers which could provide them support, as discussed by Vadysinghe et al. (2018). This implies that the vehicle design needs to be reinvestigated to ensure that it extends necessary supports for individuals to feel safe while riding. Interestingly, individuals in the low-income category showed negative scores on both two factors, suggesting a lack of concern with the latent safety features. This could be because most of the passengers of this category are captive users who do not have many options and thus stick to the most affordable means of public transport (Suman et al., 2016). However, identifying the exact reasons is a matter of further investigation. Finally, individuals from car-owning households displayed greater concern about the unsafe vehicle dynamics than those from non-car owning households. This may be because individuals from car-owning households would have noticed the absence of safety features such as seat belts in electric rickshaws which they perceived as necessary for safe riding (many of these safety features are already available in cars). In similar studies of car owners, researchers have found that car owners consider safety features—ABS, seat belts, etc.—as important in new vehicles and are willing to pay more for occupant safety (Kaul et al., 2010; Farmer and Lund, 2015). Moreover, individuals from car-owning households are generally from higher socio-economical groups (Dargay, 2001), and it has been observed that there is an increased likelihood of seatbelt usage among higher socioeconomical groups (Fhanér and Hane, 1973). Further, in related research, Vadysinghe et al., (2018) found that being thrown out of a toppling vehicle from not wearing the seatbelts is the major cause of fatal crashes in the case of three-wheelers. Many researchers have also acknowledged that the stability of three-wheelers is far less than that of four-wheelers (see Van Valkenburgh et al., 1982). All these findings would be related to the perceptions of high-income respondents about the safety of electric rickshaws. In order to explore the impacts of the dimensions of safety on the overall safety perceptions of electric rickshaw riders, an ordinal logit model was estimated. The estimation results indicate that individuals are homogenous in terms of their evaluation of the overall safety of electric rickshaws, and the unsafe structure and dynamics of electric

4. Discussion Understanding the safety preferences of users is of the utmost importance in developing safety-related guidelines and standards for any new vehicle. It becomes easier to plan efficient safety measures if we better understand the passengers’ perceptions of safety (Daziano, 2012). This study therefore attempted to understand the perceptions of passengers regarding safety in electric rickshaws using a primary attitudinal survey data. Previous studies suggest that riders expressed a sense of fear while travelling in electric rickshaws (Bhasin and Bhardwaj, 2014), but hardly any actual studies of users’ experiences exist in the academic literature. The survey was implemented in the city of Patna in India, and behaviour responses from 388 participants were utilised for the study. The study initially explored passengers’ attitudes toward different safety attributes of electric rickshaws. The important findings from the descriptive analysis suggest that the riders perceive that they would sustain severe impact in case of crashes due to the light body of electric rickshaws (Code A2, mean score = 4.582). The light bodies in paratransit services have been one of the causes of injury risks to threewheeled cycle-rickshaw riders (Jaiswal et al., 2006; Meena et al., 2014). It appears the electric rickshaw riders are also vulnerable to injury risk due to the light vehicle frame. Furthermore, fast-moving cars and buses in mixed traffic stream can also lead to severe injuries to the occupants of electric rickshaws. According to Mohan et al. (1997), existing three-wheelers in India are not safe for the occupants even at a low-impact velocity of about 15–20 km/h. Likewise, the respondents are also concerned about the open-structure of electric rickshaws (Code A3, mean score = 4.265) and the lack of protection at the rear (Code A6, mean score = 4.113) (see Fig. 1). The unstable and unsupported physical structure and design of three-wheelers make them prone to fatal crashes (Batool et al., 2018) and the respondents would be evaluating the same in the context of electric rickshaws. Further, mixed traffic flow conditions in Indian cities also pose a significant safety risk from multiple vehicle collisions due to the limited crashworthiness of three-wheelers (Mani and Pant, 2012). The descriptive analysis also shows that the respondents agree that electric rickshaws are unsafe for small children (Code A10, mean score = 4.490), possibly due to the lack of several safety features. According to MoRTH (2017), drivers’ negligence is also one of the commonest causes of road crashes in India. Previous studies have found that electric rickshaw drivers have inadequate knowledge about safe driving rules and vehicle maintenance (Goswami et al., 2018). However, the participants in this study are almost indistinguishable regarding the driving behaviour of electric rickshaw drivers (Code A4, mean score = 3.518). Furthermore, studies show that the patterns of fatal injuries in cases of three-wheelers are somewhat different from those of two-wheelers and four-wheelers. The commonest anatomical regions of fatal injuries are the head, which mostly happens due to being thrown out of the vehicle in the absence of seat belts (Vadysinghe et al., 2018). Surprisingly, most of the passengers perceive electric rickshaws as not requiring a seat belt (Code A9, mean score = 2.966). However, given the traffic situation of Indian roads and the safety risk of electric rickshaws, further vehicle design may consider the inclusion of seat belts (Dash, 2018). Finally, the descriptive statistics suggest that the passengers are not satisfied with the overall safety of the electric rickshaws (Code AA, mean score = 2.363). This is a matter of concern because individuals give highest priority to vehicle safety when evaluating the overall vehicle performance (Koppel et al., 2008). The factor analysis of the 12-item measurement questionnaire yielded two latent safety constructs, ‘unsafe structural design’ and ‘unsafe vehicle dynamics’, encompassing passengers’ perceptions related to such issues as light body, fear of overturning, lack of safety 8

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features in the analysis could be another venue for future research. It would also be interesting to know people’s willingness to pay for improvements in the safety features of electric rickshaws, as highlighted in the present study.

rickshaws represent relevant dimensions of overall safety evaluations. Shiwakoti et al. (2019), using an ordinal logit model, concluded that age and gender had no significant roles in passengers’ perceptions of safety at airports. The findings allow us to conclude that passengers attach equal importance to both constructs when assessing the overall safety of electric rickshaws. Most importantly, the negative signs of both constructs in the present study indicate that the passengers’ evaluation of overall safety is related to the structural design and vehicle dynamics of the electric rickshaws. This implies that policy-makers and vehicle designers should improve the structure, crash barrier features, and operating aspects of the current electric rickshaws so that the riders will consider rickshaws a safe mode of transport.

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5. Conclusion Able to offer an environmental-friendly and energy-efficient mobility option, electric rickshaws have gained popularity in Indian cities due to their flexible service and affordability. However, the safety of the occupants remains a serious issue, as they are among the most vulnerable road users in India due to the prevailing mixed traffic conditions (Chawla et al., 2003; Laxman et al., 2010). The present work indicates that the riders of electric rickshaws in Patna are concerned with a variety of features related to the structure and dynamics of this mode of transport. The study reveals that the indirect measures considered to evaluate the safety concerns yield two distinct safety-related factors, the unsafe structure and unsafe dynamics of the electric rickshaw. In summary, these two factors increase the safety risk of the passengers of electric rickshaws in mixed traffic flow conditions. It is found that small children, females, and elderly people (including physically disadvantaged groups) are at a higher risk. The overall safety perceptions about the electric rickshaws reveal that the people of Patna in general are not satisfied with the present form and design of the vehicle. Furthermore, in order to attract car users (in view of the aim of achieving a sustainable transport system), there is a particular need to enhance the safety levels of electric rickshaws. From an application point of view, the study shows that there is a need for crashworthiness and safety evaluation for electric rickshaws. Further, the safety evaluations and prototype testing for all electric rickshaws should strictly adhere to the Central Motor Vehicle Rules (Amendment), 2015, Government of India. The risk of multiple vehicle collisions can be reduced with certain interventions in transport infrastructure, such as providing separate lanes for the slow-moving threewheelers and introducing traffic calming devices to reduce high speeds on urban roads. Furthermore, the drivers should obtain specialised training to operate electric rickshaws before obtaining a driving licence. Finally, in order to promote electric rickshaws as a sustainable mode of public transport, this study can serve as a basis for further research to identify research priorities and to assist in traffic planning and safety legalisation of electric rickshaws. 6. Limitations and future directions This study, the first of its kind in the literature, has attempted to explore the safety aspects of three-wheeled electric rickshaws from passengers’ perspectives. The scope of the study was limited to a medium-sized city and applied statistical analysis to the data from a small sample. Hence, in order to frame safety guidelines for electric rickshaws, the study should be replicated in major metropolitan cities in India using practical sampling approaches. The research estimated independent models to explore the safety perceptions and overall safety evaluation of riders due to the limited sample size. It is expected that advanced models, such as structural regression models, could be estimated to control intercorrelations among measures and to arrive at ‘robust’ estimates of coefficients associated with the explainers. Furthermore, the study did not explicitly incorporate the driving environment and mixed traffic situations in the city. The addition of such 9

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