Accurate short-term traffic forecasts help people choose transportation and travel time. Through the query data, many models for traffic flow prediction have neglected the temporal and spatial correlation of traffic flow, so that the prediction accuracy is limited by the accuracy of traffic data. This paper proposed a short-term traffic flow prediction model that combined the spatio-temporal analysis with a Gated Recurrent Unit (GRU). In the proposed prediction model, firstly, time correlation analysis and spatial correlation analysis were performed on the collected traffic flow data, and then the spatiotemporal feature selection algorithm was employed to define the optimal input time interval and spatial data volume. At the same time, the selected traffic flow data were extracted from the actual traffic flow data and converted into a two-dimensional matrix with spatio-temporal traffic flow information. The GRU was used to process the spatio-temporal feature information of the internal traffic flow of the matrix to achieve the purpose of prediction. Finally, the prediction results obtained by the proposed model were compared with the actual traffic flow data to verify the effectiveness of the model. The model proposed in this paper was compared with the convolutional neural network (CNN) model and the GRU model, and the results show that the proposed method outperforms both in accuracy and stability.

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Short-Term Traffic Flow Prediction Method for Urban Road Sections Based on Space–Time Analysis and GRU

Customized public transit, as a diversified passenger service mode proposed under the new situation, not only deals with the comfortable and convenient commuting needs of most commuters, but also improves the efficiency of road use effectively and alleviates the road traffic pressure in the morning and evening rush hours. Currently, various routing optimization methods based on static passenger requests have been applied to the optimization design of customized bus routing in major cities, but the static passenger requests cannot satisfy the dynamic passenger requests of random users at present Therefore, it is urgent and necessary to investigate real-time customized bus routing optimization methods based on static passenger requests and dynamic passenger requests. In this paper, our goal is to present an approach to tackle two important issues of real-time customized bus route optimization under stochastic user demand: one is that how to deal with dynamic travel requests in a specific road network to improve passengers’ satisfaction with customized bus services; the other is that how to employ specific methods to achieve real-time updating of customized bus routes to meet the random user’s travel requests. Starting from the whole operation process of customized bus, we firstly construct a multi-objective optimization model of customized bus route, and clearly depicts the four processes of customized bus operation in the form of mathematical expressions. Second, we design a two-stage method based on NSGA-II algorithm to solve the model, in which the first stage deals with static bus travel requests to obtain the initial customized bus route optimization scheme, and the second stage deals with dynamic bus travel requests to update the customized bus route in real time. Finally, a two-stage case is illustrated to verify the correctness and validity of the model and the method for solving the real-time customized bus route optimization problem under stochastic user demand.

Multi-objective optimization of real-time customized bus routes based on two-stage method

To understand the status quo of urban recurrent traffic congestion, the current results of recurrent traffic congestion, and gating control are reviewed from three aspects: traffic congestion identification, evolution trend prediction, and urban road network gating control. Three aspects of current research are highlighted: (a) The majority of current studies are based on statistical analyses of historical data, while congestion identification is performed by acquiring small-scale traffic parameters. Thus, congestion studies on the urban global roadway network are lacking. Situation identification and the failure to effectively warn or even avoid traffic congestion before congestion forms are not addressed; (b) correlation studies on urban roadway network congestion are inadequate, especially regarding deep learning, and considering the space-time correlation for congestion evolution trend prediction; and (c) quantitative research methods, dynamic determination of gating control areas, and effective countermeasures to eliminate traffic congestion are lacking. Regarding the shortcomings of current studies, six research directions that can be further explored in the future are presented.

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Evolution Regularity Mining and Gating Control Method of Urban Recurrent Traffic Congestion: A Literature Review

Aiming at route optimization problem of hazardous materials transportation in uncertain environment, this paper presents a multiobjective robust optimization model by taking robust control parameters into consideration. The objective of the model is to minimize not only transportation risk but also transportation time, and a robust counterpart of the model is introduced through applying the Bertsimas-Sim robust optimization theory. Moreover, a fuzzy C-means clustering-particle swarm optimization (FCMC-PSO) algorithm is designed, and the FCMC algorithm is used to cluster the demand points. In addition the PSO algorithm with the adaptive archives grid is used to calculate the robust optimization route of hazmat transportation. Finally, the computational results show the multiobjective route robust optimization model with 3 centers and 20 demand points’ sample studied and FCMC-PSO algorithm for hazmat transportation can obtain different robustness Pareto solution sets. As a result, this study will provide basic theory support for hazmat transportation safeguarding.

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A Multiobjective Route Robust Optimization Model and Algorithm for Hazmat Transportation

To comprehensively understand the research progress of the fleet scheduling for hazardous materials, the study has summarized the corresponding research results from three aspects (a) hazardous materials transportation risk, (b) route optimization, and (c) fleet scheduling, and then pointed out potential problems from six aspects: (a) the coupling risk of the transport fleet; (b) the screening of time and space for the transport of hazardous materials; (c) the scheduling optimization for transport fleets; (d) taking insufficient account of transport risks fairness; (e) insufficient robustness of scheduling schemes; and (f) lacking of research results on fleet scheduling of transport in the context of antiterrorism. After that, by considering the existing shortcomings of the current research, five research directions are presented that should be further explored in the future. Subsequently, both rough set and association rule theory is applied to explore the cause chain of transportation accidents for hazardous materials, and analyze the mechanism of transport accident for hazardous materials. Next, the Bayesian network is presented to predict the accident rate of hazardous materials transportation under different temporal and spatial conditions, and the dynamic rolling scheduling method of hazardous materials transport fleet is constructed under normal and antiterrorism background.

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Fleet scheduling optimization of hazardous materials transportation: a literature review

For the layout problem of rural highway network, which is often characterized by a cluster of geographically dispersed nodes, neither the Prim algorithm nor the Kruskal algorithm can be readily applied, because the calculating speed and accuracy are by no means satisfactory. Rather than these two polynomial algorithms and the traditional genetic algorithm, this paper proposes an improved genetic algorithm. It encodes the minimum spanning trees of large-scale rural highway network layout with Prufer array, a method which can reduce the length of chromosome; it decodes Prufer array by using an efficient algorithm with time complexity o(n) and adopting the single transposition method and orthoposition exchange method, substitutes for traditional crossover and mutation operations, which can effectively overcome the prematurity of genetic algorithm. Computer simulation tests and case study confirm that the improved genetic algorithm is better than the traditional one.

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An Improved Genetic Algorithm for the Large-Scale Rural Highway Network Layout

Urban traffic congestion is one of the most severe problems of everyday life in many cities. In an effort to deal with this problem, traffic control system technologies have concentrated in recent years on dealing with urban congestion. In the paper, a compound mechanism particle swarm optimisation (CMPSO) algorithm has been proposed, and the algorithm has been effectively used in dealing with the optimisation of city latent ringlike road traffic control system. In the algorithm, the tabu search algorithm and the particle swarm algorithm are combined effectively, and the tabu can offset the local optimums and avoid searching one solution repeatedly. Simulations show the CMPSO algorithm cannot only avoid premature convergence effectively, but also has powerful optimisation ability and higher precision. It makes the rate of delay and numbers of stopping reduce obviously.

Latent ringlike road traffic control system based on compound mechanism particle swarm optimisation algorithm

For logistics distribution route problem, this paper presents an improved genetic algorithm, combined with the thought of tree coded, to solve single vehicle logistics distribution route problem. The algorithm adopted Prufer array coding reducing three units length of chromosome, and the decoding method did not increase time complexity of chromosome decoded to calculate objective function value when compared with the traditional coding method which coded chromosome according to sequence traversing each node. In order to prevent the feasibility of solution being destructed by the traditional crossover and mutation operators, a single transposition operator and an ortho-position exchanging operator were also designed. The following cases show that the algorithm has a higher solving efficiency and is a great help to solve large-scale route optimisation problem.

Cunrui Ma

Papers

An Improved Genetic Algorithm for the Large-Scale Rural Highway Network Layout

Latent ringlike road traffic control system based on compound mechanism particle swarm optimisation algorithm

Optimisation algorithm for logistics distribution route based on Prufer codes