A new generation of bike-sharing services without docking stations is currently revolutionizing the traditional bike-sharing market as it dramatically expands around the world. This study aims at understanding the usage of new dockless bike-sharing services through the lens of Singapore's prevalent service. We collected the GPS data of all dockless bikes from one of the largest bike sharing operators in Singapore for nine consecutive days, for a total of over 14 million records. We adopted spatial autoregressive models to analyze the spatiotemporal patterns of bike usage during the study period. The models explored the impact of bike fleet size, surrounding built environment, access to public transportation, bicycle infrastructure, and weather conditions on the usage of dockless bikes. Larger bike fleet is associated with higher usage but with diminishing marginal impact. In addition, high land use mixtures, easy access to public transportation, more supportive cycling facilities, and free-ride promotions positively impact the usage of dockless bikes. The negative influence of rainfall and high temperatures on bike utilization is also exhibited. The study also offered some guidance to urban planners, policy makers, and transportation practitioners who wish to promote bike-sharing service while ensuring its sustainability.

The failure to estimate friction on the road is a significant cause of roadway departure crashes. It is even more relevant to autonomous vehicles (AVs) as they are currently not designed to measure the skid resistance of roads. Most traditional measurements are too inefficient for real-time AV control. Thus, in this paper, we propose a dynamic method to estimate pavement friction level using computer vision. We collect 100 sets of high-quality road images and their resistance values to train the model, and design two major methods for analysis: 1) texture identification, consisting of grayscale-enhanced local binary pattern and the gray-level gradient co-occurrence matrix and 2) a deep neural network based on domain knowledge (TLDKNet). We introduce two standards of classification for model training and propose three indices--underestimation error, overestimation error, and accuracy--to verify the performance of our algorithms. The results showed the great correlation between pavement texture and skid resistance. The TLDKNet yielded the best performance with an accuracy of 90.67% and only 2.67% underestimation error, showing that it is sufficiently conservative with regard to safety. Based on the proposed method of estimation, a framework for anti-skid driving control is developed regarding the car-following behavior and turning movements. The anti-skid control framework provides new insights into enhancing the AV safety performance.

The paper studies the impacts of built environment (BE) on the first- and last-mile travel modal choice. We select Singapore as a case study. The data used for this work is extracted from the first- and last-mile trips to mass rapid transit (MRT) stations in the Household Interview Travel Survey of Singapore in 2012 with nearly 24,000 samples. The BE indicators are quantified based on four “D” variables: Density, Diversity, Design, and Distance to transit. We also take into account sociodemographic and trip-specific variables. Mixed logit (ML) modeling frameworks are adopted to estimate the impact of BE and the heterogeneity of taste across the sample. Based on the availability of light rail transit (LRT) in different areas, two modeling structures are implemented with binary ML models for non-LRT areas where “walk” and “bus” are the available travel modes, and multinomial ML models for areas where LRT is an additional alternative. The modeling results shed light on the following findings: BE—especially distance to MRT station, transportation infrastructures, land-use mix, and socioeconomic activities—significantly influences the first- and last-mile travel behaviors. Those who live or work close to MRT stations and in an area with high socioeconomic activities and land-use mix may have stronger preferences to walk for the first- and last-mile trips. The impact of physical BE (i.e., distance, infrastructures) is relatively homogeneous among the sample, while the impact of socioeconomic BE factors (i.e., floor space density, entropy) tends to vary across the sample.

This paper envisions a multimodal passenger-and-package sharing (PPS) network for urban logistics integrating metro, taxi, and truck. A hub-and-spoke structure is designed including hubs located at metro stations and service stores connected to the hubs. Packages are transported by metro on backbone links between the hubs and are carried by taxis or trucks between service stores and hubs, depending on the unit costs of these two modes and capacity constraint of the taxi. A mixed integer linear programming model for hub location problems—fusing the multiassignment p-hub median problem without capacity constraints and the capacitated multiassignment p-hub covering problem—is formulated to optimize the multimodal PPS network. The model is implemented based on the real-world data in Shanghai (China) under a series of scenarios to evaluate the network performance from two perspectives: the number of hubs and the proportion of taxi drivers who are willing to carry packages. The scenarios show that with increased number of hubs, the spatial distribution of hubs disperses from the city center to peripheral areas and more areas can be serviced by taxis. There is, however, a trade-off between the operation cost saved by taxis and the establishment cost of an extra hub. The analysis also presents that if the proportion of taxis willing to carry packages associates with the incentive payments to taxi drivers, an optimal value of incentives exists, by balancing the operation costs of taxis and trucks.