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In a battery electric bus (BEB) network, buses are scheduled to perform timetabled trips while satisfying time, energy consumption, charging, and operational constraints. Increasing research efforts have been dedicated to the integrated optimization of multiple planning tasks to reduce system costs. At a high integration level, this study determines the BEB scheduling and charging planning with flexible charging and timetable shifting strategies. We first formulate an integrated arc-based model to minimize the total costs considering the power grid pressure cost and subsequently reformulate it into a two-stage model, for which we develop an effective solution method. The first stage minimizes the total operational costs including the fleet, charging, and battery degradation costs based on the column generation technique, and the second stage minimizes the peak power demand through two timetable shifting strategies. It is found through numerical experiments that the proposed integrated optimization model and solution method can achieve significant improvement in the utilization rate and reductions in the fleet size, operational costs, and peak power demand compared to the two baseline models.

Knowledge about mobility patterns has become increasingly important to urban development. In this work, public transit origin-destination (OD) mobility patterns are undergoing meso-level analysis in using the advantages of big data and for the creation of a new planning and decision-based tool. An ensemble clustering method is proposed to abstract the common OD pairs by fully considering link-based information, and the nonnegative tensor factorization model is adopted to effectively extract and visualize quantitatively the mobility patterns of OD pairs. This is attained by using multi-layered analysis such as of traffic demand, traffic accessibility and traffic congestion to enable different visual and quantitative mobility patterns. In the case study of Beijing, these patterns were analyzed and discussed by temporal and spatial factors. The results of the various patterns show explicitly when and where to provide remedies to traffic problems, by time and space. This is analogue, to some extent, to detecting and treating black spots of road accidents. The new developed multi-layered mesoscopic analysis could, therefore, be an important tool for improving urban planning, public transit planning, traffic management, and emergency intervention.

Locating the bottlenecks in cities where traffic congestion usually occurs is essential prior to solving congestion problems. Therefore, this paper proposes a low-frequency probe vehicle data (PVD)-based method to identify turn-level intersection traffic congestion in an urban road network. This method initially divides an urban area into meter-scale square cells and maps PVD into those cells and then identifies the cells that correspond to road intersections by taking advantage of the fixed-location stop-and-go characteristics of traffic passing through intersections. With those rasterized road intersections, the proposed method recognizes probe vehicles’ turning directions and provides preliminary analysis of traffic conditions at all turning directions. The proposed method is map-independent (i.e., no digital map is needed) and computationally efficient and is able to rapidly screen most of the intersections for turn-level congestion in a road network. Thereby, this method is expected to greatly decrease traffic engineers’ workloads by providing information regarding where and when to investigate and solve traffic congestion problems.

Mobility patterns at region level can provide more macroscopic and intuitive knowledge on how people gather in or depart from the region. However, the analysis and prediction of regional mobility patterns have yet to be effectively addressed. In light of this, using smart card data (SCD) and points of interest (POI) data, a multi-step methodology which integrates the inner-restricted fuzzy C-means clustering, nonnegative tensor factorization and artificial neural network are proposed and implemented in this paper. It overcomes the difficulties in region division, pattern extraction, and prediction. The bus SCD and POI data in Beijing city are utilized for proving the usefulness of the methodology. The regional mobility patterns of bus travellers in Beijing city are extracted from the third-order tensors involving 1110 regions, 34 time slots, and 7 days of the week. The analyzed results show that the proposed methodology has a good performance on predicting the regional mobility patterns based on the regional properties. Furthermore, by considering both of the regional boarding and alighting patterns, the predictions of the regional aggregation pattern can also be achieved. These research achievements can not only provide a deep insight on the human mobility patterns at region level, but also support the evidence-based and forward-looking urban planning and intelligent transportation management.

The security travel of freight vehicles is of high societal concern and is the key issue for urban managers to effectively supervise and assess the possible social security risks. With continuous improvements in motion-based technology, the trajectories of freight vehicles are readily available, whose unusual changes may indicate hidden urban risks. Moreover, the increasing high spatial and temporal resolution of trajectories provides the opportunity for the real-time recognition of the abnormal or risky vehicle motion. However, the existing researches mainly focus on the spatial anomaly detection, and there are few researches on the real-time temporal anomaly detection. In this paper, a grid-based algorithm, which combines the spatial and temporal anomaly detection, is proposed for tracing the risk of urban freight vehicles trajectory by considering local temporal window. The travel time probability distribution of vehicle historical trajectory is analyzed to meet the time complexity requirements of real-time anomaly calculation. The developed methodology is applied to a case study in Beijing to demonstrate its accuracy and effectiveness.

Indoor simulator and on-road instrumented vehicle are the most popular ways to analyze driving behaviors by using collected Vehicle Sensor Data (VSD). However, for a same driver, the driving perfor...

The free-floating bike-sharing (BS) system plays an important role in connection with the public transit system. However, few studies have addressed the impacts of the subway network on the BS system and integrated the features quantitatively into the BS trip prediction framework. Based on the observation of the close relationship between the BS and the urban rail transit, our study focuses on the trip forecasting of the BSs around the subway stations. Firstly, the subway station related sites are investigated based on the BS dataset in Beijing, China. Secondly, multiple categories of features are extracted, including the subway station related site categories by clustering, the BS site mobility patterns by tensor decomposition, as well as other features (e.g., temporal, POI, meteorological, and air quality information). Finally, a three-layer ensemble learning model based method (i.e., the SAP-SF method) under the stacking strategy is proposed with integrations of multiple features and the several selected machine learning algorithms. It is applied to the simultaneous prediction of the hourly return numbers for a large-scale BS system involving a total of 280 sites in Beijing. The output performance is also examined by comparing the results with those obtained from the benchmark models. It is indicated that the features of subway station related site categories and site mobility patterns jointly contribute to the improvement of BS trip prediction. The accuracy can be increased layer by layer and is superior to the single machine learning algorithm. The research finding can provide useful information for system administrators to perform service level checks and to rebalance BSs around subway stations.

Building human-centered intelligent transport systems (ITSs) requires thorough understanding of the diversified driving styles among drivers. In data-driven driving behavior studies, the temporal distance is deemed as an important variable. However, with respect to the driving style analysis, the appropriate temporal distance range has not been clear yet, and little attention has been drawn to the larger temporal distance that may also have a potential effect on driving style. This paper proposes a new three-layer structure of driving style by using the modified latent Dirichlet allocation (mLDA) model. It is found that the results revealed by the mLDA model based on real driving behavior data are able to align themselves with the results from a driving style questionnaire, and some self-reporting bias is uncovered. More comprehensive driving styles are discovered quantitatively, and the appropriate temporal distance range for driving style analysis is determined. The analyzed results indicate that the time-gap range larger than 10 s are still pivotal and the time-gap range below 20 s is a suitable range for driving style analysis.

Mixed motor vehicle and bicycle traffic is increasingly common in urban transport. Multi-phase crossings for left-turning bicycles are proposed for highly populated intersections to reduce conflict and improve safety. The objective of this research was to study the potential impact of applying multi-phase crossings for left-turning bicycles on the efficiencies of signalised intersections by means of microscopic simulation. Based on research on the behaviour of motor vehicle drivers and cyclists, a microscopic simulation model was upgraded to consider mixed traffic comprising motor vehicles and bicycles in urban networks. The model was validated with data collected from the road network in Beijing, China. The simulation results show that bicycle multi-phase crossings with proper signal operations can reduce bicycle delays dramatically with only a minor effect on vehicle delay, and can reduce the number of vehicle stops.