Travel demand and distance analysis for free-floating car sharing based on deep learning method.

In order to address the time pattern problems in free-floating car sharing, in this paper, the authors offer a comprehensive time-series method based on deep learning theory. According to car2go booking record data in Seattle area. Firstly, influence of time and location on the free-floating car-sharing usage pattern is analyzed, which reveals an apparent doublet pattern for time and dependence usage amount on population. Then, on the basis of the long-short-term memory recurrent neural network (LSTM-RNN), hourly variation in short-term traffic characteristics including travel demand and travel distance are modeled. The results were also compared with other different statistical models, such as support vector regression (SVR), Autoregressive Integrated Moving Average model (ARIMA), single and second exponential smoothing. It showed that (LSTM-RNN) shows better performance in terms of statistical analysis and tendency precision based on limited data sample

Lane-based vehicular speed characteristics analysis for freeway work zones using aerial videos

Work zones which are usually referred to as traffic bottlenecks or critical points may lead to reductions in both operational performance and safety. Researchers have conducted extensive studies on speed characteristics in freeway work zones. However, most of these studies are based on simulation data or are travel direction-based rather than lane-based. By utilizing the video recording capability of drones, this paper aims to provide methodologies and the workflow in exploring the vehicular speed characteristics in freeway work zones with considerations for lane and location deviations. A four-to-three lane work zone on Huhang Expressway between Downtown Shanghai and Songjiang in China was selected for data collection. Then free flow speed variation pattern is analyzed, a speed prediction model is proposed, and the speed–flow relationship is investigated on each lane at nine virtual sections along the work zone. It is found that the merging area affects the speeds mainly in the segment from 200 m upstream of the taper to the start of the activity area, which is the most important risk area in the work zone, and the speed characteristics depend on vehicle types (trucks versus automobiles) as well as lane locations. It is also showed that drones are valuable and effective tools for data collection for vehicular speed characteristics analysis in freeway work zones and it is easy to extract the information needed.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Connected Vehicle Technology for Improved Multimodal Winter Travel: Agency Perspective and a Conceptual Exploration

Accurate and real-time traffic and road weather information acquired using connected vehicle (CV) technologies can help commuters perform safe and reliable trips. A nationwide survey of transit operation managers/supervisors was conducted to assess the suitability for CV transit applications in improving the safety and mobility during winter weather. Almost all respondents expressed positive attitudes towards the potential of CV applications in improving winter transit travel and voiced their concerns over the safety consequences of CV equipment failure, potential of increased driver distraction, and reliability of system performance in poor weather. A concept of operations of CV applications for multimodal winter travel was developed. In the conceptual framework, route-specific road weather and traffic flow data will be used by the transit managers/supervisors to obtain real-time operational status, forecast operational routes and schedules, and assess operational performance. Subsequently, multimodal commuters can receive the road-weather and traffic-flow information as well as transit routes and schedule information

A Crash Severity Prediction Method Based on Improved Neural Network and Factor Analysis

Crash severity prediction has been raised as a key problem in traffic accident studies. Thus, to progress in this area, in this study, a thorough artificial neural network combined with an improved metaheuristic algorithm was developed and tested in terms of its structure, training function, factor analysis, and comparative results. Data from I5, an interstate highway in the Washington State during the period of 2011–2015, were used for fitting and prediction, and after setting the theoretical three-layer neural network (NN), an improved Particle Swarm Optimization (PSO) method with adaptive inertial weight was offered to optimize the NN, and finally, a comparison among different adaptive strategies was conducted. The results showed that although the algorithms produced almost the same accuracy in their predictions, a backpropagation method combined with a nonlinear inertial weight setting in PSO produced fast global and accurate local optimal searching, thereby demonstrating a better understanding of the entire model explanation, which could best fit the model, and at last, the factor analysis showed that non-road-related factors, particularly vehicle-related factors, are more important than road-related variables. The method developed in this study can be applied to a big data analysis of traffic accidents and be used as a fast-useful tool for policy makers and traffic safety researchers