Multimodal Transportation最新文献

This study aims to explore the spatial and temporal dynamics of e-scooter usage and its association with the built and social environmental attributes in Minneapolis, Minnesota. Using a 4-month long trip data from 2019, this street-segment-level (N=13,367) study deployed negative binomial regression to build trip generation and destination models. The extent of the impact of built and social environment-related attributes varies at different temporal dimensions of e-scooting. Regarding spatial pattern, university areas and areas close to downtown generated substantially higher trips than other Minneapolis areas. In terms of temporal pattern, trips are more diffused at night and morning, and more concentrated in midday and evening. The high percentage of residential, commercial, and institutional land uses, a higher land use mix, high-valued parcels, and more food-related Points of Interest are strongly associated with e-scooter usage. E-scooter is heavily used in areas with more bike-dependent commuter, less car-dependent commuter, and young-aged people. Further, the presence of a sidewalk, bike-share station, bus stop, and transit shelter on a street segment positively affect scooter usage. These findings will assist e-scooter operators and city planners to formulate policies. Further, by strengthening the literature, this study can serve as a foundation for cities new in e-scooter mobility.

Urban road pricing is presented as an effective instrument in the context of public transportation policies to improve sustainability in large cities and their surroundings. Among the main problems of its implementation is the lack of social acceptance. The present article, through a population survey, aims to analyze the social acceptability in the implementation of an urban toll in the city of Dakar to identify the effectiveness of this measure on mobility patterns and the advantages and disadvantages declared by citizens as indicators of the degree of social acceptability. The results of the research reveal a set of significant implications in the field of transportation policies that serve as the basis for strategic decision making and to ensure an adequate level of social acceptability of the proposed toll system.

The road infrastructure maintenance is crucial for hassle-free transportation. The proposed work leverages the dense connectivity of public transportation buses for road condition monitoring at a large scale. It uses the buses equipped with GPS and accelerometer as mobile sensors to infer the road surface roughness and the damaged road segments. The vibration features are computed from accelerometer data using the in-bus controller, and trip records are processed offline using a centralized server. The proposal applies the unsupervised learning based Self Organizing Map (SOM) and $k$-means clustering algorithms on the GPS location records and vibration features to infer the road condition. The damaged segments and rough patches of the selected region that requires immediate repairment are suggested. This information can be used to prioritize the repairment based on the available time and budget.

The proposed solution is evaluated using more than 1150 km of trip records collected over four routes of Gujarat state of India. The proposed solution accurately infers the road roughness and identifies the damaged road segments for maintenance. Moreover, the ablation analysis of the proposal is carried out to evaluate the utility of combined execution of SOM and $k$-means algorithms. Further, the feasibility of proposal for a large scale deployment is assessed. The analysis shows that the proposed system is scalable and can process the daily transit data of a metro-city (e.g. 540 buses of the Ahmedabad Municipal Transport Service) using the in-bus controllers and a server.