Bike-sharing holds promise for available and healthy mobility services during COVID-19 where bike sharing users can make trips with lower health concerns due to social distancing compared to the ...restricted transportation modes such as public transit and ridesharing services. Leveraging the trip data of the Divvy bike-sharing system in Chicago, this study exploresspatially heterogeneous effects of built environment on bike-sharing usage under the pandemic. Results show that the average weekly ridership declined by 52.04%. To account for the spatially heterogeneous relationship between the built environment and the ridership, the geographically weighted regression (GWR) model and the semiparametric GWR (S-GWR) model are constructed. We find that the S-GWR model outperforms the GWR and the multiple linear regression models. The results of the S-GWR model indicate that education employment density, distance to subway, COVID-19 cases, and ridership before COVID-19 are global variables. The effects between ridership and the built environment factros (i.e., household density, office employment density, and the ridership) vary across space. The results of this study could provide a useful reference to transportation planners and bike-sharing operators to determine the high bike-sharing demand area under the pandemic,thus adjusting station locations, capacity, and rebalancing schemes accordingly.
•Study the alternative mode choice of ride-sourcing users based on survey data.•54.71% of respondents choose public transit when the ride-sourcing is suspended.•Age, gender, income, car ownership, ...and trip purpose are significant determinants.•More metro stations around the trip origin could reduce private vehicle use.•Suspension of ride-sourcing services would lead to lower vehicle emissions.
Despite the popularity of ride-sourcing services among the public, some cities consider suspending ride-sourcing services because of repeated safety failures and protests from taxi drivers. The impact of such a policy on the transportation system and environment is rarely studied. To fill this gap, we performed a stated preference survey in Chengdu, China, to investigate the alternative mode choice (AMC) of ride-sourcing users. We collected data of 435 trips from 340 respondents. The analysis results show that with suspension, 53.71%, 27.36%, 10.80%, and 7.13% of ride-sourcing trips would shift to public transit, taxi, active mode (walking and cycling), and private car trips, respectively. The mixed logit modeling results show that age, gender, household income, number of cars per person, trip purpose, and transit accessibility significantly influence AMC. Results of Monte-Carlo Markov Chain simulations indicate that vehicle emissions related to ride-sourcing trips will decrease by more than 50% after the suspension.
The New York City (NYC) initiated a new default speed limit law on November 7th, 2014, where speed limits on all road segments without a posted speed limit were reduced from 30 mph to 25 mph. The ...safety effectiveness of citywide speed limit reduction in an urban setting like NYC has been understudied in the literature. The high-density road network of NYC could lead to a significant spatial spillover effect of speed limit reduction on its neighboring sites. In addition, citywide speed limit reduction exerts much more treatment sites than control sites, which makes it challenging to identify sufficient control sites with similar covariates as the treated ones and thus may lead to confounding bias. Furthermore, there could also exist a time trend in crash observations caused by unobserved factors (e.g., enforcement, driving behaviors). To jointly account for spatial spillover effect, confounding bias, and time trend, this study proposes a novel causal inference approach integrating propensity score matching (PSM) and spatial difference-in-differences (SDID) to estimate the safety effectiveness of citywide speed limit reduction in NYC. The PSM utilizes a logistic generalized additive model (GAM) to capture the nonlinear relationship between covariates and the treatment indicator to reduce bias due to confounding variables. Moreover, the matched data are used to develop the SDID model that simultaneously captures spatial spillover effect and time trend via the extended difference-in-differences (DID) structure. The proposed causal approach suggests that the speed limit reduction would result in a 62.09% decrease in fatal crashes, with the spatial spillover effect found to be statistically significant. However, it does not indicate a significant change in injury and property-damage-only crashes as a result of the speed limit reduction. This study adds to the literature a robust causal inference approach for safety evaluation and provides researchers, practitioners, policy-makers insights into the safety effectiveness of the speed limit reduction in an urban setting.
Though the collection of metro smart card data could help improve the operations of the metro system, the release of such data might lead to privacy issues. Few studies have quantified the ...probability to re-identify a user from the smart card data using very limited trajectory points. Thus, this study investigates this topic by analyzing eight-day metro smart card data of Chengdu, China. Results reveal that, on the macro level, three random trajectory points with a temporal resolution of one minute and one hour are enough to identify over 90% and 67% of the users. Even when the resolution is reduced to one day, 20% of the users could be still be identified by three points. On the individual level, three carefully selected points with a temporal resolution of one minute, one hour, and one day could lead to a re-identification risk no less than 0.5 for 99%, 89%, and 52% of the users. The effects of number of points, number of users, and other temporal resolutions are also thoroughly evaluated. These findings emphasize the great privacy issues involved in the release of metro smart card data and remind metro operators to take proactive measures to enhance privacy protection.
•Uniqueness and re-identification risk of metro users in trip data are quantified.•Three random trajectory points could identify over 90% of the users.•Three points could raise the re-identification risk of 99% of the users up to 0.5.•Effects of number of points, number of users, and temporal resolutions are evaluated.•Results reveal the privacy issues involved in the release of metro smart card data.
The emergence of ridesharing services might complement or substitute public transit systems, leading to intricate relationships between the two services. However, limited studies focused on the ...nonlinear effects of ridesharing use frequency on public transit usage. Therefore, this paper investigated such nonlinear effects using the hierarchical negative binomial generalized additive model (HNBGAM), with the latest publicly available National Household Travel Survey (NHTS) dataset. The negative binomial and hierarchical negative binomial generalized linear models were also developed for comparison with the HNBGAM. The NHTS data involved travel information of 928 ridesharing users within 98 census tracts in San Diego. Two-level hierarchy (individual and census tract level) was constructed in the HNBGAM. In addition, the smooth function of the HNBGAM could help identify the nonlinear effects of ridesharing use frequencies on public transit usage. Demographic factors (age, gender, race, household size, etc.) and built environment factors (e.g., population density, worker density, percentage of rental houses, and house unit density) were also considered in the modeling process. The findings revealed a negligible impact on public transit usage for occasional ridesharing use (from one to eleven times per month), a complementary effect for regular ridesharing use (from eleven to thirty-two times per month), and a substitution effect for active ridesharing use (more than thirty-two times per month). Understanding such nonlinear relationships could help policymakers make more informed decisions to avoid the over-substitution of public transit usage and better complement the public transport system.
•The HNBGAM identified the nonlinear effects and spatial dependence simultaneously.•Built environment factors were considered in the modeling process.•Occasional ridesharing use had negligible impacts on public transit usage.•Regular ridesharing use would hugely complement public transit usage.•Active ridesharing use would substitute public transit usage.
Despite many prior studies about the determinants of city-level private car ownership in China, limited studies investigate the effects of vehicle regulation policies and relative price (the ratio of ...the average car price to GDP per capita) on city-level private car ownership simultaneously. Thus, panel data of 212 cities in China from 2006 to 2015 were collected to explore these effects. The potential explanatory variables include relative price, vehicle regulation policies, socio-economic factors, urban characteristics of the city, and transportation-related factors. The pooled model, fixed-effects model, and random effects model are constructed to analyze the panel data. With the fixed effect on the temporal trend, the fixed-effects model turns out to be the best. Variables including relative price, license plate lottery, vehicle use restriction based on the last digit of the license plate, the average salary of employed workers, and the number of taxis per 10,000 population are all found significant. Supportive policies for alternative fuel cars do not significantly affect private car ownership. One percent increase in the relative price is associated with a 0.08% decrease in private car ownership. The license plate lottery and vehicle use restriction policies would reduce private car ownership by 18.94% and 7.7%, respectively. The finding of this study could provide a helpful reference for policymakers to develop appropriate measures to control the growth of private car ownership of a city.
•Determinants of city-level car ownership are revealed based on data of 2006–2015.•Pooled OLS model, fixed-effects model, and random-effects model are compared.•Supportive policies for alternative fuel vehicles do not influence car ownership.•1% increase in relative car price is related to 0.08% decrease in car ownership.•Implementation of vehicle use restriction leads to 7.7% decrease in car ownership.
Safety service patrols (SSPs) play an important role in incident management on highways. It is critical to respond to incidents in a timely manner as this can significantly reduce nonrecurrent ...congestion and improve safety. Therefore, it is essential to allocate available SSP vehicles to highway segments such that their effectiveness is maximized. This study aimed to develop a simulation-based framework to assist with SSP service optimization. More specifically, a discrete event-based simulation tool (i.e., SSP-OPT) with customizable parameters was developed to help plan the optimum patrol routes based on available SSP resources and predicted incidents. The developed tool was tested with roadway traffic and incident data from the Virginia highway network. After model calibration, the simulation results showed that the developed SSP-OPT tool could replicate the patrol routes with similar performance to the field observations, validating the tool. Further, adopting the tool for corridor-level optimization could help to identify the best patrol plan to minimize SSP response time and maximize SSP response rates for a given number of SSP vehicles. The SSP-OPT tool requires minimal user input (e.g., segment lengths, annual average daily traffic) and has the flexibility to be easily applied to any highway corridor once calibrated. The tool generates various performance metrics to enable more informed decision making in SSP route planning.
•Spatial and temporal variation of ridesplitting adoption rates was analyzed.•Impacts of fare and built environment on ridesplitting adoption were explored.•Nonlinear models (GAM) outperformed the ...linear models (linear regression).•A middle-level fare discount 0.23 is effective in improving ridesplitting.•Locations with high potential for ridesplitting improvement were determined.
As a new mode of shared mobility that allows users to share the same trip (vehicle) with others at a low travel cost, ridesplitting reduces environmental pollution and eases traffic congestion. Although the relationship between the built environment and the ridesplitting adoption rates has been explored before, few studies investigated the effect of fare discounts on the ridesplitting adoption rate (proportion of ridesplitting trips to ride-hailing trips) while controlling for the origin and destination characteristics. Thus, we explored this topic by analyzing the ride-hailing trip data of Chicago from January to May 2019. The generalized additive model was used to investigate the nonlinear impacts of built environment variables (e.g., population density and employment density) and travel attributes (fare discount and median trip distance) on ridesplitting adoption rates. One notable finding is that the fare discount is most effective in improving ridesplitting adoption rates when its value is around 0.23. In addition, because the trip fare is rounded to the nearest $2.50, a sensitivity analysis was performed to make sure that the approximation had a limited impact on the study results. Finally, the origin–destination (OD) pairs with a high potential for improving the ridesplitting adoption rate were identified. These OD pairs are the trips related to the airports and the trips from the north to downtown. The findings can help transportation planners and government agencies identify the areas for ridesplitting improvement and provide guidelines for transportation network companies to set appropriate fare discounts for ridesplitting.
Although many studies have investigated the correlations between injury severities and seat positions, few researchers explored the correlates of injury severities (e.g., seat positions) within a ...crash that results in multiple occupant injuries. Therefore, we examine the injury correlates within and between crashes, and study the correlations between seat positions and occupant injury severity by constructing a hierarchical ordered probit model. A total of 20,327 occupant injuries in 16,405 motor vehicle crashes in South Australia (2012 − 2016) are used. The results of this study indicate that the rear left passenger seat is associated with a 7.66% higher chance of getting injured (including moderate and severe injury), and the front left passenger seat is associated with a 2.94% higher chance of getting injured compared with the driver seat. Besides, the higher injury chances for other passenger seats including the rear right and rear middle seats are 4.97% and 4.74%, respectively, compared with the driver seat. Thus, this study offers passengers insightful suggestions about how to protect themselves by choosing the right passenger seat in a vehicle.
The rapid development of ride-hailing services has sparked debate about their role in urban transport. While there are several studies exploring impacts of ride-hailing services on transportation ...systems, little work has been done to study mode choice transitions of ride-hailing users. This paper investigates ride-hailing use in November 2018 in Chengdu, China. We use survey results to depict usage characteristics of ride-hailing trips and users. A binary logit model is utilized to investigate factors influencing mode transitions because we suspect that the previous mode has an important effect on the future mode choice (when ride-hailing services is banned or heavily restricted). Ride-hailing services caters specially to younger and educated respondents. Although total shares of car-based modes (drive alone, taxi, get a ride with friends /family) does not show obvious changes, many respondents would shift away from taxi towards transit, bike, and driving alone. Besides, some respondents who used transit previously would be more likely to choose taxi. Our study could offer insights for relative regulations and policies.
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