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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.

Long-term travel time prediction, ahead of making a trip, is vital from the planning perspective of delivery freight, timetable design, vehicle/crew scheduling and further activities. The better the prediction is, the higher the reliability of service that can be offered. This study presents a discrete and continuous combined analysis for attaining improved long-term travel time prediction (LTTP) of commercial vehicles. One main problem of LTTP is that the speed factors are unknown ahead of trips. In light of this, the nonnegative tensor factorization and completion with neural weighted initialization is proposed to extract the potential speed patterns among multiple discrete factors and to complete the sparse tensors. The Gaussian mixture regression is adopted for handling the continuous factors. The proposed methodology with a combined discrete and continuous analysis is able to effectively integrate multiple factors into the computation, including vehicle type, road type, days, time period, weather conditions, driver differences and travel distance. The methodology is able to reduce the long-term travel time prediction error between 14% and 43% compared with the traditional average speed method and other baseline methods, which suggests its effectiveness. It can strategically assist policy-making processes of stakeholders on investment, insurance, planning and management, and can help tactically in predicting long-term travel time ahead of the scheduled trips to improve the reliability of the schedules. Furthermore, operationally, it can also be used to enrich current navigation information systems by separately predicting the commercial vehicles’ travel time based on multiple factors.

The terminal control area airspace system is one of the most complicated subsystems in the entire air traffic control system. With the continuous increase of air traffic volume, the utilization of its airspace resources has become a bottleneck restricting the sustainable development of the system. Monitor and obtain its operating status information and formulating corresponding mitigation measures are essential to improve the overall performance of the system. In order to achieve that, a new system analysis method based on clustering of trajectory characteristic point is proposed. The flight trajectories from radar position data are reconstructed at first. Followed by the method of extracting flight trajectory characteristic points including turning points, altitude transition points and speed adjustment points, all characteristic points are extracted. Then, K-means is applied for clustering according to their 3D space position, while the results are analyzed by introducing evaluation indexes based on air traffic control operation strategies. Numerical experiments show significant clustering characteristics and revealing the operation status of the terminal area airspace, which can help air traffic manager to identify defects in airspace operation and providing guidance for future airspace planning and flight procedure adjustment.

Overtaking maneuvers occur when vehicle drivers pursue higher driving speeds or comfort scenarios through back-to-back lane-changing behaviors, which require active participation of mental resources and certain self-learning practices. However, few studies have investigated how brain activities change during overtaking. Moreover, the learning process, which indicates the heterogeneity of drivers from a process-based perspective, has been neglected. In this work, we studied varied overtaking and learning styles using electroencephalogram (EEG) signals collected from drivers during a simulated driving task with a possible learning process. The average speed, standard deviation of speed, steering wheel angle and lateral movement distance of overtaking behaviors are analyzed in these reinforced tasks to evaluate overtaking performance. Four types of overtaking styles (i.e., low-speed type, low-speedstrong-oscillation type, high-speedstrong-steering type, and high-speedclose-distance type) and three types of learning styles (i.e., stable, adaptive and changeful) are discovered, not only from eventual overtaking behaviors but also from behavioral changes in a certain learning process. EEG features, such as the power spectral density (PSD) in the θ, α, β and γ bands, are extracted to characterize driver mental states and to correlate with heterogeneous learning styles. The obtained results show that fatigue and fatigue confrontation are more likely with a stable learning style, and the mental workload is reduced with an adaptive learning style, whereas no significant changes in brain activity are apparent with a changeful learning style. Understanding and recognizing heterogeneous overtaking and learning styles with varying EEG patterns will be extremely useful in the future for deep integration of advanced driving assistance systems (ADASs) and brain computer interface (BCI) systems.

Bus scheduling plays a significant role in public transportation and supports the sustainable development of transportation systems. Challenges are beginning to appear with the newly emerging electric buses (EBs), as scheduling changes due to fleet composition make traditional fixed timetables no longer able to satisfy operational needs. Moreover, the fixed-trip time hypothesis has been inappropriate for large cities due to the variety of urban traffic statuses. This paper proposes an optimal framework for reforming the mixed operation schedule for electric buses and traditional fuel buses under stochastic trip times. Based on the primary grouping genetic algorithm (GGA), a straightforward framework with a Monte Carlo simulation is presented to optimize the scheduling scheme. Case studies based on the operating environment and service trips of real bus lines in Beijing are conducted to verify the effectiveness of the proposed model by considering both the composition of fleet types and time stochasticity. Additionally, the impacts of stochasticity, fleet composition, government subsidies and cost factors on operational costs are investigated. Considering stochastic trip times, the achieved scheduling strategies can provide the optimal proportion of electric and traditional fuel buses and make a crucial impact on operational costs. This work was supported by the National Natural Science Foundation of China under Grants No. 71961137008; the Fundamental Research Funds for the Central Universities No.2020YJS096; the National Natural Science Foundation of China under Grants No. 71621001 and 91746201; JPI Urban Europe project ‘SMUrTS’ under Grant No. 299078.

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.

With the explosion of information in our current era, senders of information increasingly need to target their messages to recipients. However, messages within transportation systems, including traffic information and commercial advertisements, tend to be transmitted to all drivers indiscriminately. This is because the information providers (such as other vehicles, roads, facilities, buildings etc.), can hardly recognize the variations within drivers, who should be treated differently as information recipients. As a result of the rapid development of data collection technologies and machine learning techniques in recent years, extraction and recognition of drivers’ unique driving style from actual driving behaviour data become possible. In this paper, two kinds of topic models are investigated: mLDA and mHLDA, to discover distinguishable driving style information with hidden structure from the real-world driving behaviour data. The results show that the proposed models can successfully recognize the differences between driving styles. The study is of great value for providing deep insight into the underlying structure of driving styles and can effectively support the recognition of drivers with different driving styles.

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.

Unlike personal cars for daily commuting, freight vehicles demonstrate vastly different traveling behaviors with longer spatial-temporal activity that is composed of multiday trip chains. Q...

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.

Noninvasive EEG signals provide neural activity information at high resolution, of which human mental status can be properly detected. However, artefacts always exist in brain oscillatory EEG signals and thus impede the accuracy and reliability of relevant analysis, especially in real-world tasks. Moreover, the use of a mature artefact identification method cannot assure impeccable artefact separation; this leads to a trade-off between removing contaminated information and losing valuable information. This study addresses this problem by investigating a simulator-based driving behaviour analysis using a car-following scenario to correlate the EEG-based mental features with behavioural responses. The study develops an architecture for an artefact composition pool and proposes three integrated prediction models to evaluate the removal compositions of the EEG artefacts. Three errors (mean absolute, root mean square, mean absolute percentage) and R-squared index are considered for measuring the performance of the models. The results show that the best-performing composition outperformed the no-removal and all-removal cases by 11.75% and 4.28% improvements, respectively. Specifically, we investigate different common artefacts including eye blinks, horizontal eye movements, vertical eye movements, generic discontinuities and muscle artefacts. The gained knowledge on artefact removal, EEG spectral features and stimuli-response patterns can be further applied to properly manipulate real-world EEG signals and develop an effective brain-computer interface.

In contrast to a large number of studies in the route choice behavior with absolute bounded rationality, little concern is laid on that with relative bounded rationality which is hypothetically sug...

One of the most important questions concerning language processing in the bilingual brain is whether the two languages share the same neural structure for comprehension [1-3].

Situational discomfort awareness plays an important role in decision making among drivers and has rarely been discussed in detail in previous research. An instrumented vehicle was used to collect car-following data from multiple drivers, thereby quantitatively examining situational discomfort grading patterns using a new discomfort grading method and the latent Dirichlet allocation model. In this process, the gas pedal data and speed difference data are particularly involved in the computation for providing broader meaning to discomfort and building more comprehensive situations. The results show that individual discomfort awareness varies between drivers. More importantly, the potential patterns of situational discomfort grading are extracted, which provides knowledge for characterizing drivers in the context of discomfort awareness. The knowledge achieved can be further applied to distinguish drivers and identify the typical comfort and discomfort zones. This study has great value for promoting investigations on traffic psychology and developing more effective and customized driver assistant systems.

Accurately understanding driving behaviour is of crucial importance for advanced driving assistant systems such as adaptive cruise control system and intelligent forward collision warning system. To understand different driving styles, this study employs the clustering method and topic model to extract latent driving states, which can elaborate and analyse the commonness and individuality of driving behaviour characteristics with the longitudinal driving behaviour data collected by the instrumented vehicle. To handle the large set of data and discover the valuable knowledge, the data mining techniques including ensemble clustering method based on the kernel fuzzy C-means algorithm and the modified latent Dirichlet allocation model are employed in this study. The 'aggressive', 'cautious' and 'moderate' driving states are discovered and the underlying quantified structure is built for the driving style analysis. Language: en

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.

With the rapid expansion of the railway represented by high-speed rail (HSR) in China, competition between railway and aviation will become increasingly common on a large scale. Beijing, Shanghai, and Guangzhou are the busiest cities and the hubs of railway and aviation transportation in China. Obtaining their supply configuration patterns can help identify defects in planning. To achieve that, supply level is proposed, which is a weighted supply traffic volume that takes population and distance factors into account. Then supply configuration can be expressed as the distribution of supply level over time periods with different railway stations, airports, and city categories. Furthermore, nonnegative tensor factorization (NTF) is applied to pattern recognition by introducing CP (CANDECOMP/PARAFAC) decomposition and the block coordinate descent (BCD) algorithm for the selected data set. Numerical experiments show that the designed method has good performance in terms of computation speed and solution quality. Recognition results indicate the significant pattern characteristics of rail&ndash, air transport for Beijing, Shanghai, and Guangzhou are extracted, which can provide some theoretical references for practical policymakers.