Bi Yu Chen

Map-matching algorithm for large-scale low-frequency floating car data

Large-scale global positioning system (GPS) positioning information of floating cars has been recognised as a major data source for many transportation applications. Mapping large-scale low-frequency floating car data (FCD) onto the road network is very challenging for traditional map-matching (MM) algorithms developed for in-vehicle navigation. In this paper, a multi-criteria dynamic programming map-matching (MDP-MM) algorithm is proposed for online matching FCD. In the proposed MDP-MM algorithm, the MDP technique is used to minimise the number of candidate routes maintained at each GPS point, while guaranteeing to determine the best matching route. In addition, several useful techniques are developed to improve running time of the shortest path calculation in the MM process. Case studies based on real FCD demonstrate the accuracy and computational performance of the MDP-MM algorithm. Results indicated that the MDP-MM algorithm is competitive with existing algorithms in both accuracy and computational performance.

An efficient solution algorithm for solving multi-class reliability-based traffic assignment problem

The multi-class reliability-based user equilibrium (RUE) problem has been intensively studied in recent years, as it can capture the route choice behaviors of users with heterogeneous risk-aversion under demand and supply uncertainties. Few solution algorithms, however, are available for solving the RUE problems in large-scale road networks. This is mainly due to the non-additive property of the path finding sub-problem in the RUE model. An efficient traffic assignment solution algorithm for solving the multi-class RUE problems in large-scale road networks is proposed in this study. First, an effective shortest path algorithm is developed to explicitly overcome the non-additive difficulty. The algorithm is capable of finding optimal paths for all user classes in one search process and hence the repeated search process for each user class is avoided. This property can save not only computational time but also memory requirement. The proposed shortest path algorithm is then, further incorporated into a path-based traffic assignment algorithm using a column generation technique. Such traffic assignment algorithms can solve the multi-class RUE problem without the requirement of path enumeration. Experimental results show that the proposed solution algorithms can, even for large-scale networks with multi-user classes, efficiently achieve highly accurate RUE solutions within satisfactory computational time.

Reliable Shortest Path Problems in Stochastic Time-Dependent Networks

This study investigates the time-dependent reliable shortest path problem (TD-RSPP), which is commonly encountered in congested urban road networks. Two variants of TD-RSPP are considered in this study. The first variant is to determine the earliest arrival time and associated reliable shortest path for a given departure time, referred to as the “forward” TD-RSPP. The second problem is to determine the latest departure time and associated reliable shortest path for a given preferred arrival time, referred as the “backward” TD-RSPP. It is shown in this article that TD-RSPP is not reversible. The backward TD-RSPP cannot be solved by the algorithms designed for the forward problem using the reverse search from destination to origin. In this study, two efficient solution algorithms are proposed to solve the forward and backward TD-RSPP exactly and the optimality of proposed algorithms is rigorously proved. The proposed solution algorithms have potential applications in both advanced traveler information systems and stochastic dynamic traffic assignment models.

Estimating Real-Time Traffic Carbon Dioxide Emissions Based on Intelligent Transportation System Technologies

In this paper, a bottom-up vehicle emission model is proposed to estimate real-time CO2 emissions using intelligent transportation system (ITS) technologies. In the proposed model, traffic data that were collected by ITS are fully utilized to estimate detailed vehicle technology data (e.g., vehicle type) and driving pattern data (e.g., speed, acceleration, and road slope) in the road network. The road network is divided into a set of small road segments to consider the effects of heterogeneous speeds within a road link. A real-world case study in Beijing, China, is carried out to demonstrate the applicability of the proposed model. The spatiotemporal distributions of CO2 emissions in Beijing are analyzed and discussed. The results of the case study indicate that ITS technologies can be a useful tool for real-time estimations of CO2 emissions with a high spatiotemporal resolution.

On-time delivery probabilistic models for the vehicle routing problem with stochastic demands and time windows

Increasing attention is given to on-time delivery of goods in the distribution and logistics industry. Due to uncertainties in customer demands, on-time deliveries cannot be ensured frequently. The vehicle capacity may be exceeded along the planned delivery route, and then the vehicle has to return to the depot for reloading of the goods. In this paper, such on-time delivery issues are formulated as a vehicle routing problem with stochastic demands and time windows. Three probabilistic models are proposed to address on-time delivery from different perspectives. The first one aims to search delivery routes with minimum expected total cost. The second one is to maximize the sum of the on-time delivery probabilities to customers. The third one seeks to minimize the expected total cost, while ensuring a given on-time delivery probability to each customer. Having noted that solutions of the proposed models are affected by the recourse policy deployed in cases of route failures, a preventive restocking policy is examined and compared with a detour-to-depot recourse policy. A numerical example indicates that the preventive restocking policy can help obtain better solutions to the proposed models and its effectiveness depends on the solution structure. It is also shown that the third model can be used to determine the minimum number of vehicles required to satisfy customers’ on-time delivery requirements.

Spatiotemporal data model for network time geographic analysis in the era of big data

ABSTRACT There has been a resurgence of interest in time geography studies due to emerging spatiotemporal big data in urban environments. However, the rapid increase in the volume, diversity, and intensity of spatiotemporal data poses a significant challenge with respect to the representation and computation of time geographic entities and relations in road networks. To address this challenge, a spatiotemporal data model is proposed in this article. The proposed spatiotemporal data model is based on a compressed linear reference (CLR) technique to transform network time geographic entities in three-dimensional (3D) (x, y, t) space to two-dimensional (2D) CLR space. Using the proposed spatiotemporal data model, network time geographic entities can be stored and managed in classical spatial databases. Efficient spatial operations and index structures can be directly utilized to implement spatiotemporal operations and queries for network time geographic entities in CLR space. To validate the proposed spatiotemporal data model, a prototype system is developed using existing 2D GIS techniques. A case study is performed using large-scale datasets of space-time paths and prisms. The case study indicates that the proposed spatiotemporal data model is effective and efficient for storing, managing, and querying large-scale datasets of network time geographic entities.

Reliable Space–Time Prisms Under Travel Time Uncertainty

Time geography is a powerful framework for analyzing human activities under various space–time constraints. At the core of time geography is the concept of the space–time prism, which delimits an individuals potential activity locations in space and time. The classical space–time prism, however, admits only deterministic travel speeds and ignores the stochastic nature of travel environments. In this article, the classical space–time prism model is extended to congested road networks with travel time uncertainty. A reliable space–time prism is proposed to consider explicitly an individuals on-time arrival probability concerns in the face of travel time uncertainty. The reliable space–time prism is defined as the set of space–time locations where an individual can participate in an activity and return to his or her destination with a given on-time arrival probability. To construct such a reliable space–time prism in a road network, a solution algorithm is developed. A case study using real-world traffic information is carried out to demonstrate the applicability of the proposed prism model. The results of the case study indicate that the proposed prism model can represent well individuals’ space–time taking into account various on-time arrival probability concerns.

A Hybrid Link-Node Approach for Finding Shortest Paths in Road Networks with Turn Restrictions

Turn restrictions, such as ‘no left turn’ or ‘no U-turn’, are commonly encountered in real road networks. These turn restrictions must be explicitly considered in the shortest path problem and ignoring them may lead to infeasible paths. In the present study, a hybrid link-node Dijkstras (HLND) algorithm is proposed to exactly solve the shortest path problem in road networks with turn restrictions. A new hybrid link–node labelling approach is devised by using a link–based labelling strategy at restricted nodes with turn restrictions, and a node-based labelling strategy at unrestricted nodes without turn restrictions. Computational results for several real road networks show that the proposed HLND algorithm obtains the same optimal results as the link-based Dijkstras algorithm, while having a similar computational performance to the classical node-based Dijkstras algorithm.

Most reliable path-finding algorithm for maximizing on-time arrival probability

ABSTRACT Finding the most reliable path that maximizes the probability of on-time arrival is commonly encountered by travelers facing travel time uncertainties. However, few exact solution algorithms have been proposed in the literature to efficiently determine the most reliable path in large-scale road networks. In this study, a two-stage solution algorithm is proposed to exactly solve the most reliable path problem. In the first stage, the upper and lower bounds of on-time arrival probability are estimated. Dominance conditions and the monotonic property of the most reliable path problem are then established. In the second stage, the multi-criteria label-setting approach is utilized to efficiently determine the most reliable path. To illustrate the applicability of the proposed solution algorithm, a comprehensive case study is carried out using a real road network with stochastic travel times. The results of case study show that the proposed solution algorithm has a remarkable computational advantage over the existing multi-criteria label-correcting algorithm.

Measuring place-based accessibility under travel time uncertainty

ABSTRACT Travel time uncertainty has significant impacts on individual activity-travel scheduling, but at present these impacts have not been considered in most accessibility studies. In this paper, an accessibility evaluation framework is proposed for urban areas with uncertain travel times. A reliable space-time service region (RSTR) model is introduced to represent the space-time service region of a facility under travel time uncertainty. Based on the RSTR model, four reliable place-based accessibility measures are proposed to evaluate accessibility to urban services by incorporating the effects of travel time reliability. To demonstrate the applicability of the proposed framework, a case study using large-scale taxi tracking data is carried out. The results of the case study indicate that the proposed accessibility measures can evaluate large-scale place-based accessibility well in urban areas with uncertain travel times. Conventional place-based accessibility indicators ignoring travel time reliability can significantly overestimate the accessibility to urban services.