Taxis play an indispensable role as the main mode of paratransit in many cities in the developing countries. To the best of our knowledge, research on taxi service quality, and certainly studies focusing on developing countries, are quite rare. Using a subjective approach (data collected from passengers), this study examines the main determinants that affect user’s perception of fixed-route taxi services in Shiraz, Iran. The methodology adopted in this study employs principle component analysis (PCA) followed by partial least squares structural equation modeling (PLS-SEM). The results show that among the different attributes of service quality, driver behavior (including components of attitude toward passengers, driving practice, and professionalism) was the strongest determinant of passengers’ overall assessment of the service quality. Service providers and the municipalities that oversee operations can draw on the results of this study to develop future strategies for improving their paratransit services.

Road traffic accidents (RTAs) rank in the top ten causes of the global burden of disease and injury, and Iran has one of the highest road traffic mortality rates in the world. This paper presents a spatiotemporal analysis of intra-urban traffic accidents data in metropolitan Shiraz, Iran during the period 2011-2012. It is tried to identify the accident prone zones and sensitive hours using Geographic Information Systems (GIS)-based spatio-temporal visualization techniques. The analysis aimed at the identification of high-rate accident locations and safety deficient area using Kernel Estimation Density (KED) method. The investigation indicates that the majority of occurrences of traffic accidents were on the main roads, which play a meta-region functional role and act as a linkage between main destinations with high trip generation rate. According to the temporal distribution of car crashes, the peak of traffic accidents incident is simultaneous with the traffic congestion peak hours on arterial roads. The accident-prone locations are mostly located in districts with higher speed and traffic volume, therefore, they should be considered as the priority investigation locations to safety promotion programs.

This paper exploits the potential of Global Positioning System datasets sourced from mobile phones to estimate the racial composition of road users, leveraging data from their respective Census block group. The racial composition data encompasses approximately 46 million trips in the Chicago metropolitan region. The research focuses on the relationship between camera tickets and racial composition of drivers vs. police stops for traffic citations and the racial composition in these locations. Black drivers exhibit a higher likelihood of being ticketed by automated speed cameras and of being stopped for moving violations on roads, irrespective of the proportion of White drivers present. The research observes that this correlation attenuates as the proportion of White drivers on the road increases. The citation rate measured by cameras better matches the racial composition of road users on the links with cameras than do stops by police officers. This study therefore presents an important contribution to understanding racial disparities in moving violation stops, with implications for policy interventions and social justice reforms.

Data on pedestrian infrastructure is essential for improving the mobility environment and for planning efficiency. Although governmental agencies are responsible for capturing data on pedestrian infrastructure mostly by field audits, most have not completed such audits. In recent years, virtual auditing based on street view imagery (SVI), specifically through geo-crowdsourcing platforms, offers a more inclusive approach to pedestrian movement planning, but concerns about the quality and reliability of opensource geospatial data pose barriers to use by governments. Limited research has compared opensource data in relation to traditional government approaches. In this study, we compare pedestrian infrastructure data from an opensource virtual sidewalk audit platform (Project Sidewalk) with government data. We focus on neighborhoods with diverse walkability and income levels in the city of Seattle, Washington and in DuPage County, Illinois. Our analysis shows that Project Sidewalk data can be a reliable alternative to government data for most pedestrian infrastructure features. The agreement for different features ranges from 75% for pedestrian signals to complete agreement (100%) for missing sidewalks. However, variations in measuring the severity of barriers challenges dataset comparisons.