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The provision of lane-level travel time information can enable accurate traffic control and route guidance in urban roads with distinctive traffic conditions among lanes. However, few studies in the literature have been conducted to estimate lane-level travel time distributions. This study proposes a new vehicle re-identification (V-ReID) method for estimating lane-level travel time distributions using video images from widely deployed surveillance cameras. In the proposed method, a lane-based bipartite graph matching is introduced to obtain optimal matches between upstream and downstream vehicles by considering lane-level traffic conditions and vehicles' lane changing behaviors and visual features. A lane-based travel time estimation technique is introduced to real-time estimate full spectrum of lane-level distribution parameters, including not only the mean but also the standard deviation and the distribution type. A comprehensive case study is carried out on a congested urban road in Hong Kong. Results of case study show that the proposed method outperforms the state-of-the-art link-based V-ReID method and is capable for providing accurate lane-level travel time distribution information on congested urban roads.

Over the past decades, the impact of transport operating strategy improvements on space-time accessibility, which is an important research area for network design problem, has not been explicitly i...

Recent years have witnessed an increasing use of big data in mobility research. Such efforts have led to many insights on the travel behavior and activity patterns of people. Despite these achievem...

Insufficient network capacity and conflicts between pedestrians and vehicles at roadway intersections can be critical obstacles to the operational efficiency of evacuation activities. Reducing pedestrian—vehicle conflict points and expanding network capacities are two possible approaches to improving operational efficiency, especially when network accessibility varies in different evacuation stages. This paper integrates two types of network reconfiguration strategies, namely, the use of contraflow lane reversal for road lanes and pedestrian walkways and time-dependent conflict point elimination by separating pedestrian and vehicle flows with physical barriers at road intersections, to strategize pedestrian and vehicle moving directions during a mass evacuation. A multiobjective optimization model is formulated to adaptively select the appropriate locations for barriers according to different evacuation phases, and the model is solved by a modified genetic algorithm-based heuristic approach. An experiment on optimizing an urban regional evacuation network configuration in the case of a toxic gas leak accident was carried out to validate the proposed model. The numerical results show an increase of approximately 15% in the maximum evacuee throughput and a reduction of approximately 65% in the average exposure risk of evacuees of the optimal plans compared with the uncontrolled plan.

In recent years, the development of information and communication technologies (ICTs) has made it technically and economically feasible to collect huge amounts of spatiotemporal data about human mo...

Vehicular traffic between different Origin-Destination (OD) pairs for a typical hourly period may statistically correlate with each other. The covariance mainly generated from the daily variation of travel patterns, network topology, and trip chaining activities of household members can be particularly high during the morning peak hour. With the increasing attention on the OD demand variance and covariance in stochastic road networks, a new criterion is proposed in this paper for measuring the estimation accuracy of OD demand covariance. The mathematical properties of this proposed criterion are analyzed to better understand the relationship between the estimation errors of mean and covariance of OD demands. This paper aims to investigate how to optimize the traffic count locations for minimizing the weighted maximum deviation of estimated mean and covariance of OD demands from the “true” values. To consider the effects of stochastic OD demands on the traffic count location problem in the proposed model, link choice proportions are regarded as stochastic variables and updated by an adapted traffic flow simulator in this study. Both the weighted-sum approach and bi-objective approach are examined with the adaption of the firefly algorithm (FA) to solve the single-objective and bi-objective problems. Numerical examples are presented to demonstrate the effects, with and without considering the covariance of the OD demands for the optimization of traffic count locations.

Most current accessibility equity studies ignore travel time uncertainties. This study investigates the travel time uncertainty impacts on the equity of individual accessibility. Travel time distributions of the road network and mobility data for a large number of individual samples across the entire study area are extracted using comprehensive big datasets of taxi trajectories and mobile phone tracking data. Two reliability-based individual accessibility measures are proposed to evaluate individual accessibility by explicitly considering individual’s on-time arrival probability concern for activity participations. The proposed measures are further applied to quantify travel time uncertainty impacts on the equity of individual accessibility to shopping services. Results of this study demonstrate the capabilities of using spatiotemporal big data to examine the equity of accessibility in a disaggregated individual level. The results also suggest that travel time uncertainties have negative impacts on accessibility of all people groups, but more serious impacts on disadvantaged people groups with a lower accessibility level.

Conventional location-based accessibility measures are static and cannot represent accessibility fluctuations at different times of day. To fill this gap, this study proposes location-based space-time accessibility measures to capture the temporal variation of location-based accessibility. Using the space-time utility perspective, the accessibility of a location is conceptualized as the space-time utility offered by a set of facilities accessible from the location. Individuals' facility choice behaviors among multiple alternatives are explicitly considered. A time-dependent facility attractiveness function is introduced to represent the temporal variation of individuals' needs to perform activities at a certain facility. The introduced function is formulated as two components: a time-invariant component representing individual satisfactions derived from activity participation at the facility, and a time-varying component expressing individuals' dynamic intensities to perform a certain type of activities at different times of day. To demonstrate the applicability of these proposed measures, a comprehensive case study has been carried out in Wuhan, China. The results of the case study show that the proposed measures can well capture the temporal variation of accessibility, due to the dynamics both of traffic conditions and of individuals' intensities in performing activities at different times of day. The proposed measures require moderate level of data, in terms of rich facility information; and most of these data could be extracted from social media applications.

In recent years, human mobility research has attracted much attention in various research fields. The individual activity space is an important indicator for quantifying human mobility and has been widely used in numerous studies of human geography, e.g., accessibility and segregation. In the literature, various methods have been proposed to estimate individual activity spaces. However, most existing methods build on a single-center structure. Further, most existing methods generally lack of capabilities to capture heterogeneous visit probability within the activity space. In this paper, we propose a new method for estimating individual activity spaces with visit probability. The individual activity space is explicitly formulated as a multiple-center structure with multiple separated parts. Then, several performance metrics are developed to quantify the estimation accuracy of activity spaces. To demonstrate the effectiveness of the proposed method, a case study using longitude GPS trajectories was carried out. The result of case study shows that the proposed method had a higher estimation accuracy of individual activity spaces than the state-of-the-art methods particularly for individuals with large radius of gyration.

Point cloud registration is essential for processing terrestrial laser scanning (TLS) point cloud datasets. The registration precision directly influences and determines the practical usefulness of TLS surveys. However, in terms of target based registration, analytical point cloud registration error models employed by scanner manufactures are only suitable to evaluate target registration error, rather than point cloud registration error. This paper proposes an new analytical approach called the registration error (RE) model to directly evaluate point cloud registration error. We verify the proposed model by comparing RE and root mean square error (RMSE) for all points in three point clouds that are approximately equivalent.

Many existing accessibility studies ignore human mobility due to the lack of large-scale human mobility data. This study investigates the impacts of human mobility on accessibility using massive mo...

This paper proposes a bi-objective reliable path-finding algorithm for routing battery electric vehicles on a road network, with vehicles’ energy consumption uncertainty and travel time uncertainty. A bi-objective stochastic optimization problem is proposed and formulated to simultaneously maximize energy consumption reliability (ECR) and travel time reliability (TTR). ECR is defined as the probability of finishing a trip without exhausting a given battery energy budget, while TTR is the on-time arrival probability with the travel time budget. In this study, the proposed optimization problem is decomposed into two sub-problems: (1) finding K most reliable paths for maximizing the TTR objective and (2) finding the most reliable path for optimizing the ECR objective. Then, a novel ranking algorithm is proposed to exactly solve the formulated optimization problem. A case study is carried out on Hong Kong’s road network to demonstrate the efficacy and efficiency of the proposed algorithm for real-world applications.

This paper presents an analysis of the spatial characteristic of the modal accessibility gap (MAG) in Guangzhou based on travel times for public transport and cars to public centers calculated by the Travel O-D point Intelligent Query System (TIQS). Four spatial regression models are used to investigate the effect on the MAG of six factors of the built environment (distance to city public centers, residential density, landuse mix, bus stop density, metro station density and road network density). The results show that travel time for a given origin to destination (OD) trip is likely to be larger by public transport than by car, especially for a trip of longer travel time. The MAG values of all the communities are larger than 0, indicating that when compared with public transport, travelling by car takes less travel time for individuals. Residential density, land-use mix, bus stop density and metro rail station density have significant negative direct impacts and indirect impacts (spatial spillover effects) on MAG, which indicates that appropriate increase in community residential density, providing diversified service facilities within a community, and improving public transport supply are beneficial to promote modal shift. Specifically, the role of metro rail is greater than that of buses. However, the road network density has significant direct positive impacts on MAG, and its spillover effect is also significantly positive, which indicates that building more roads is not an effective way to narrow the accessibility gap between public transport and cars but may facilitate more car travel instead.

The vehicle routing problem (VRP) is one of the most prominent problems in spatial optimization because of its broad applications in both the public and private sectors. This article presents a novel spatial parallel heuristic approach for solving large-scale VRPs with capacity constraints. A spatial partitioning strategy is devised to divide a region of interest into a set of small spatial cells to allow the use of a parallel local search with a spatial neighbor reduction strategy. An additional local search and perturbation mechanism around the border area of spatial cells is used to improve route segments across spatial cells to overcome the border effect. The results of one man-made VRP benchmark and three real-world super-large-scale VRP instances with tens of thousands of nodes verify that the presented spatial parallel heuristic approach achieves a comparable solution with much less computing time.