2021

Saul Obregon-Biosca, Universidad Autonoma de Queretaro
Anubis Navarro Rosas, Universidad Autonoma de Queretaro

Road events have become a public health problem. The bibliography suggests that not following the traffic signals by pedestrians is one of the most common causes of accidents. This research estimates, through an observational study, four models to establish the significance variables in the pedestrians’ obedience of traffic signals in an intersection. During rush hours, 6,709 pedestrian movements were recorded in the city of Santiago de Querétaro to analyze their behavior. The variables correspond to the characteristics of the pedestrian, infrastructure, group behavior, obedience of traffic signals, interaction with vehicles and the time of day. The existence of vehicular flow, the time of day, and the fact that the pedestrian crossing is obstructed are the variables with the greatest influence on the probability that the pedestrian will not assume risk behaviors when crossing. The factors that influence the probability of obeying the pedestrian traffic light are the time of day, the existence of vehicular flow and the obedience of the pedestrian crossing; for this last are the number of lanes, the obedience to the pedestrian traffic light and if the pedestrian crossing is obstructed; while for the probability of running are the existence of vehicular flow, the obedience to the pedestrian traffic light and waiting in group. Men engage in more risky behaviors than women and people over 60 years old assume less risky behaviors than younger people. This study represents a first approach to the explanation of the variables that influence the observance of traffic signals and risk behaviors.

Fan Zhang, Southeast University
Huitao Lv, Southeast University
Yanjie Ji, Southeast University

Delivery e-bike (DEB) group has become an important part of the urban e-bike group in China. The red-light running (RLR) behavior of DEBs is a main cause of traffic accidents of this group at signalized intersections. This paper aims to explore the influencing factors of delivery and ordinary e-bike riders running red lights and compare the differences between the two e-bike groups, which help to develop countermeasures to reduce such behaviors. In this study, we employed the mixed binary logistic model to capture the unobserved heterogeneity effects. With this approach, factors such as individual characteristics, behavioral variables, characteristics of signalized intersections and traffic environment were examined. The data of e-bikes crossing behaviors at four signalized intersections in Xi'an, China were collected, and 3,335 samples of e-bikes were recorded. The results showed that delivery riders are more likely to run red lights than ordinary riders. Factors that influence RLR behaviors of the two e-bike subgroups are different. Factors associated with the unobserved heterogeneity include age, observation time, red-light stage and traffic light type. Results from the models provide insights for future development of e-bikes’ RLR behavior intervention systems to improve traffic safety at intersections.

Sajan Shaikh, Dalian University of Technology
Hong-feng Xu, Dalian University of Technology
Farrukh Baig, Dalian University of Technology
Dong Zhang (zhangdong@dlut.edu.cn), Dalian University of Technology

The technology of electric motorcycles evolved substantially in the past decades. Considering its advantage in emission and energy efficiency, it is likely to play a vital role in the future transport system. Researchers have explored the potential impacts of electric motorcycles, but few have focused on the intentions of the public to adopt; hence, the present paper aimed to examine the factors that may influence the behavioral intentions of using electric motorcycles in Pakistan. As an extension of the technology acceptance model, environmental concern, and perceived value are included with perceived usefulness, perceived easiness of use as predictors of the intention to use electric motorcycles. With a sample of 228 valid responses from Karachi, Pakistan, two structural equation models have been estimated and compared. It is found that when only perceived usefulness and perceived ease of use included, they both impose a direct influence on the adoption intention. After adding the perceived value and environmental concern, more variation of behavior intention is explained in terms of R-square. However, the impact of perceived ease of use is mediated by perceived usefulness. Policy recommendations for government and electric manufacture on purchasing intention promotion are proposed accordingly.

Subasish Das, Texas A&M University

Traffic crashes are a major public health concern, resulting in over 1.35 million deaths worldwide in 2016. The number of reported traffic fatalities in Bangladesh was 2,376 in 2016. However, the World Health Organization (WHO) estimated that the true value ranges between 20,730 to 29,177. Editorial traffic crash reports in Bangladesh vary widely in the range of crashes reported. This study collected traffic crash reports from online English daily newspapers by setting a Google News Alert. The current study prepared a database of 419 crash-related reports during the period of six months (November 2018 to April 2019). The reports contain a bag of 81,019 words. The results reveal that online news coverage tends to vary from news agency to news agency. The reports usually do not cover key contributing factors of crash occurrences. Additionally, geometric features of crash occurrence sites are rarely reported. The findings show that it is important to decipher media coverage to understand the potential safety risk measures in Bangladesh. The current findings provide strong support for the notion that there is a need for a guideline that can help the journalists adequately document a crash report in media.

Vasudevan N, Sardar Vallabhbhai National Institute of Technology
Ninad Gore, Sardar Vallabhbhai National Institute of Technology
Rupali Zope, Sardar Vallabhbhai National Institute of Technology
Shriniwas Arkatkar, Sardar Vallabhbhai National Institute of Technology
Gaurang Joshi, Sardar Vallabhbhai National Institute of Technology

The present study is focused on the habit of travel by using personal modes which is considered as one of the major factor that resist the shift behavior of choice riders to public transport. Habit developed among two-wheeler and car users is analyzed considering their psychological, socio-economic-demographic and convenience factors. Structural Equation Modeling (SEM) technique is adopted to quantify habit of travel developed in travellers. Surat, a metropolitan city in the Indian State of Gujarat is considered as the study area where the share of personal mode is 31% and that of public transport is 4%. The dismally low share of public transport since past two decades has acted as a catalyst in the development of habit of using personal modes among users in the city. The influence and the relationship between various psychological, socio-economic-demographic and convenience factors in the development of habit is very well established using SEM approach. Results elicited that the convenience in trip-chaining, desire to drive, easy access etc. influences the development of habit. Further, to attract the choice riders towards sustainable transport modes like public and non-motorized transport modes, three transit supportive policies were proposed and tested. The impact of the developed policies to attract the choice riders by breaking their habit is analyzed using SEM models. The results of the policy tests revealed that the proposed polices are feasible and are able to break the inculcated habit and can recommend to policy makers and stake holders to develop sustainable urban transport policies.

Amjad Pervez (amjadpervez04@yahoo.com), Central South University
Jaeyoung Lee, Central South University

Motorcycles constitute 74% of the total registered vehicles in Pakistan. Since the last decade, its number has been increased by 613%, showing that it is an essential and popular mode of transportation in the country. Therefore, the present study estimated a random parameter logit model to investigate the factors influencing the motorcycle injury severity using crash data of Karachi city collected through road traffic injuries surveillance system. Fatal and non-fatal crashes are used as categories of motorcyclist injury severity levels to calibrate the model. Mainly the effects of crash specific factors, motorcyclists’ characteristics, and temporal factors are considered for motorcycle-injury severity analysis. It was revealed that the probability of fatal injury increases for crashes: during the summer season, occurring on weekends, in the early morning hours or nighttime, involving teenage, young and older riders, due to female pillion rider clothes stuck in wheel, involving a heavy vehicle and single-vehicle collisions. Also, the probability of non-fatal injuries increases for crashes that involve pillion passengers and crashes where a motorcycle was involved. The research findings suggest that besides measures to control the risky motorcyclists’ behavior, there is a need to separate motorcycles from heavy vehicles and educate the female pillion rider to be careful while riding the motorcycle. The results are expected to generate more discussion and interest in motorcycle safety in the country and can be used by the enforcement agencies to improve motorcycle safety in the country, as well as in other developing countries with similar situations.

Eeshan Bhaduri, Indian Institute of Technology Kharagpur
Manoj BS, Indian Institute of Technology Kharagpur
Zia Wadud, University of Leeds
Arkopal Goswami, Indian Institute of Technology, Kharagpur
Charisma Choudhury (cfc@alum.mit.edu), University of Leeds

The COVID-19 pandemic has resulted in unprecedented changes in our travel behaviour. The limited number of research work conducted to date have demonstrated that different socio-demographic groups of different countries have changed travel behaviour in response to COVID-19 in different ways (e.g. ( 1 , 2 ) etc.). But, to the best of our knowledge, no studies to date have modelled the relationship between changes in transport mode usage and traveller characteristics in order to quantify the associated heterogeneity. In this paper, we address this gap by developing mathematical models to quantify the effect of the socio-demographic characteristics of the travellers on the mode-specific trip frequencies before (January 2020) and during the early stages of COVID-19 spread in India (March 2020). Primary data collected from 498 respondents participating in online surveys have been used to estimate multiple discrete choice extreme value models. Estimation results indicate significant presence of inertia to continue using the Pre-COVID modes, with higher propensities to shift to virtual (e.g. working from home, online shopping, etc.) and private modes (e.g. car, motorcycle) from shared ones (e.g. bus and ride-share options). The extent of inertia varies with the trip purpose (commute and discretionary) and trip lengths. The results also demonstrate significant heterogeneity based on age, income, and working status of the respondent. The findings will be directly useful for planners and policy-makers in India as well as some other countries of the Global South in better predicting the mode-specific demand levels and subsequently, making better investment and operational decisions.

Kinjal Bhattacharyya, Indian Institute of Technology Kharagpur
Saurabh Dandapat, Indian Institute of Technology Kharagpur
Sai Annam, Indian Institute of Technology Kharagpur
KAUSTUBH SAYSARDAR, Indian Institute of Technology, Kharagpur
Bhargab Maitra, Indian Institute of Technology Kharagpur

The global outbreak of Corona Virus Disease 2019 (COVID-19) has posed multifaceted challenges to the world. In order to restrict the spread of the highly contagious virus, the Government of India imposed lockdown and social distancing rules on March 24, 2020. As the experiences from lockdown are completely new, a critical scientific question arises on how to derive the social impact of the lockdown and identify the scopes for imposing more efficient and effective lockdown in the future, if the situation demands, as well as formulating future transport strategies. In this regard, the present study was aimed to bring out the evidences related to public perceptions and attitude towards travel-related activities and preventive measures during the early stages of lockdown in Indian cities. A five-stage survey instrument was designed and circulated through various online modes to make it convenient to the respondents and also to achieve a greater geographical coverage within a short period of time. The survey responses were analysed to obtain meaningful insights and derive implications in four specific aspects - attitude towards strategies and preventive measures, long-distance travel characteristics, perceptions towards essential services, and, perceptions towards post-lockdown travel. The study reports several interesting observations with respect to each of the above-mentioned aspects that may be of use to the policy and decision-makers in the near future.