The market share of e-scooters in the United States has proliferated in cities: 86 million trips were made on shared e-scooters in 2019, a more than 100% increase compared to 2018. However, the interaction of e-scooters with other road users and infrastructure remains uncertain. This study scrutinized 52 e-scooter and 79 bicycle police-reported crashes in Nashville, Tennessee, from April 2018 to April 2020 from the Tennessee Integrated Traffic Analysis Network (TITAN) database. We used descriptive analysis and a recent prototype version of the Pedestrian and Bicycle Crash Analysis Tool (PBCAT) to classify crashes based on the locations of the crashes relative to roadway segments or intersections, as well as the maneuver of the motor vehicle and e-scooter/bicycle relative to the motor vehicle. Two crash typologies can explain the majority of e-scooter crashes, while bicycle crashes are distributed over several crash typologies. Additionally, 1 in 10 e-scooter- and bicycle-motor vehicle crashes leads to the injury or fatality of the e-scooter rider or bicyclist. Furthermore, we noted statistically significant differences in spatial and temporal distribution, demographics, lighting conditions, and crash distance from home for e-scooter and bicycle crashes. The police crash report provides a comprehensive picture of e-scooter safety complementing existing literature. We found that e-scooter crash characteristics do not fully overlap with features of bicycle crashes. A generalized engineering, education, and enforcement treatment to reduce and prevent e-scooter and bicycle crashes, injuries, and fatalities might not result in equal outcomes for each mode. More rigorous enforcement could be implemented to deter e-scooters riders under the age of 18 years and e-scooter safety campaigns could target female riders.

The transition from conventional vehicles (CVs) to electric vehicles (EVs) could be promising in tackling environmental challenges in China. Using a sample of 1216 respondents in Beijing, China, our study intends to understand the underlying factors that drive the decision to purchase an EV among potential Chinese vehicle purchasers. We built two choice models to estimate vehicle purchase behavior and fuel choice. We found that males and having higher household income are associated with greater intention to purchase EVs (both plug-in and battery electric vehicles). However, a previous inclination to choose CV negatively impacted willingness to buy EVs. Between specific EV types, we found that Plug-in Hybrid EV (PHEV) purchase was negatively associated with plans to obtain a driver’s license within three years and longer durations of having owned a motorized vehicle first. Yet, the number of electric bicycles in the household was positively associated with PHEV-purchase likelihood. For Battery EVs (BEV), we found that respondents who had previous experience with an EV (either as a driver or passenger) were more likely to purchase a BEV while existing ownership of a driver’s license and a higher purchase budget reduced such possibility. Based on our findings, we recommend authorities continue to, or increasingly, provide direct monetary incentives to purchase EVs, and to provide EV driving and riding experience to customers, especially who are in the middle- and low-income vehicle purchasing groups, to improve the Chinese EV market relative to CVs.

Bike-sharing systems are becoming more and more popular both in the United States and around the world due to their easy accessibility and environmental friendliness. At the end of 2016, more than 430 bike-sharing programs and nearly 2.3 million bikes were available to the public around the world. In the United States, 123 million trips have been made using shared bikes since 2010. In 2017, the number of trips made using shared bikes has reached 35 million, which is 25\% higher than that of 2016. As an increasingly popular mode of transportation, bike-sharing systems are designed for short-distance trips, which not only improves the efficiency of short-distance travels but also alleviates traffic congestion and air pollution. In addition, interests in bike-sharing systems have been fueled by several cities, including Washington DC, where bike share has quickly become a convenient and popular transportation option. Feasibility is the first to study for parties considering implementing a bike-sharing system, detailed information such as the location of stations, number of shared bikes and especially the appropriate size of catchment area should be estimated precisely by system planners. Afterwards, with such information reasonable policies can be made to ensure the operational efficiency of system and maximize system-wide ridership. However, little information is available on what size of an appropriate catchment area should be used. Due to its simplicity in application, direct demand model is gaining more attention in demand forecasting. In this study, direct demand models were used to explore the best catchment area of the bike-sharing stations in Chicago, USA. In order to find out the best catchment area, multiple radii distance range from 200 m to 800 m were established around stations in the system. Results indicated that model fitting results do not vary among different catchment areas. Thus, for purpose of completing their studies, whatever catchment area is readily available or calculated can be used by direct demand modelers. And more attention should be paid by researchers to explore the appropriate boundaries in the future.

As a sustainable mode of transportation, subways bring great convenience to the society. Although there have been many studies examining the relationship between the built environment and the station-level ridership, those studies focused mainly on the ridership, which is defined as the number of trips for each station. While ridership is an important indicator for evaluating subway demand, passenger-distance is another critical indicator that incorporates distance into demand evaluation, which has not yet been fully explored. To fill this gap, this paper investigates the relationship between the built environment around stations and the station-level passenger-distance (SLPD). As noted in previous studies, the relationship between the built environment and travel demand can vary by space. Therefore, a geographically weighted regression (GWR) model and a mixed geographically weighted regression (MGWR) model have been used to explore this spatially varying relationship using Chengdu, China, as an example case. The results were compared with that of an ordinary least squares (OLS) model. The comparison shows that the MGWR model that considers both global and local variables has the best goodness of fit. Results also show that 11 of the 25 potential variables are significantly related to SLPD. The accessibility of the station, station type, such as transfer or terminal, number of bus stops, number of restaurants, density of building area, density of the national road network, and density of the provincial road network, all have a positive correlation with SLPD. Meanwhile, the variables, whether it is a newly opened subway station, density of living points of interest (POIs), and density of railroad network, are all negatively correlated with SLPD. Ten of the eleven significant variables (except accessibility) have spatially varying relationships with SLPD. These findings can serve a useful reference for transportation planners for the demand evaluation.

Direct ridership models can predict station-level urban rail transit ridership. Previous research indicates that the direct modeling of urban rail transit ridership uses different coverage overlapping area processing methods (such as naive method or Thiessen polygons), area analysis units (such as census block group and census tract), and various regression models (such as linear regression and negative binomial regression). However, the selection of these methods and models seems arbitrary. The objective of this research is to suggest methods of station-level urban rail transit ridership model selection and evaluate the impact of this selection on ridership model results and prediction accuracy. Urban rail transit ridership data in 2010 were collected from five cities: New York, San Francisco, Chicago, Philadelphia, and Boston. Using the built environment characteristics as the independent variables and station-level ridership as the dependent variable, an analysis was conducted to examine the differences in the model performance in ridership prediction. Our results show that a large overlap of circular coverage areas will greatly affect the accuracy of models. The equal division method increases model accuracy significantly. Most models show that the generalized additive models have lower mean absolute percentage errors (MAPE) and higher adjusted values. By comparison, the Akaike information criterion (AIC) values of the negative binomial models are lower. The influence of different basic spatial analysis unit on the model results is marginal. Therefore, the selection of basic area unit can use existing data. In terms of model selection, advanced models seem to perform better than the linear regression models.

Traditional taxi has been criticized for underserving regions far away from downtown, leading to social inequity. However, there has been no evidence so far to support this argument. With the advent of e-hailing taxi services such as Uber and Lyft, it becomes possible to test this argument. Because e-hailing taxi trips are assigned to the drivers who are willing to undertake the rides, the e-hailing taxi trips plus the traditional taxi trips can be regarded as the true travel demand served by taxi. The proportion of e-hailing taxi trips to total taxi trips is used as an indicator of to what extent area is underserved by traditional taxi. All the origins of traditional taxi and e-hailing taxi trips in New York City from January 2015 to December 2017 are aggregated at the level of taxi zones, which is defined by the New York City Taxi Limousine Commission. Fractional Response Model (FRM) is used to identify factors that influence the proportion of e-hailing trips. Results show that the proportion of African-American, the proportion of households of income less than 50,000 dollars, the proportion of household of income between 100,000 to 200,000 dollars, road density, distance to the Midtown Center have positive impact on the proportion of e-hailing taxi trips, while the population density, subway station density, bus station density, existence of airport have negative impact on the proportion of e-hailing taxi trips.