Time-varying Road Network Research Articles

Multi-Objective Planning of Commuter Carpooling under Time-Varying Road Network

Aiming at the problem of urban traffic congestion in morning and evening rush hours, taking commuter carpool path planning as the research object, the spatial correlation of traffic flow at adjacent intersections is mined using convolutional neural networks (CNN), and the temporal features of traffic flow are mined using long short-term memory (LSTM) model. The extracted temporal and spatial features are fused to achieve short-term prediction. Considering the travel willingness of drivers and passengers, a multi-objective optimization model with minimum driver and passenger loss time and total travel time is established under the constraints of vehicle capacity, time windows and detour distances. An Improved Non-dominated Sorted Genetic Algorithm-II (INSGA-II) is proposed to solve it. The open-loop saving algorithm is used to generate an initial population with better quality, and the 2-opt local search strategy is adopted in the mutation operation to improve search efficiency. The influence of vehicle speed on the matching scheme is analyzed. The research results show that under the same demand conditions, the total travel distance of the carpool scheme is reduced by about 56.19% and total travel time is reduced by about 65.52% compared with the non-carpool scheme. Research on carpool matching under time-varying road networks will help with urban commuting efficiency and environmental quality, and play a positive role in alleviating traffic congestion and promoting carpool services.

Time-varying road network design for urban hazmat transportation

Hazmat transportation in cities faces significant risks that may cause catastrophic losses to humans. From the perspective of the regulator, the main responsibility is to mitigate hazmat transport risk by determining the availability of road networks to hazmat carriers. Based on the time-variant population distribution, the hazmat transport risk was assessed via the total population exposure associated with the resident and variable populations at different times. We propose a risk-minimizing urban hazmat road network design model for multiple types of hazmats, considering time-varying traffic. The model was applied to a realistic case study of hazmat transportation in a densely populated urban area with complex traffic in Shanghai, China.

Optimization of Vehicle Paths considering Carbon Emissions in a Time-Varying Road Network

Regarding the study of the time-dependent green vehicle path problem (TDGVRP), most of the literature uses the step function to represent the vehicle speed change in order to reduce the computation, ignoring the continuity of vehicle speed, which leads to the lack of accurate carbon emissions measurement. This study represents the vehicle speed variation as a continuous function to make the constructed model more consistent with the actual situation, in order to promote the reduction of carbon emissions generated in the logistics and distribution process, improve the greenhouse effect and ecological environment, and ultimately promote sustainable development. In this paper, a simulated annealing-genetic hybrid algorithm (GA-SA) is proposed to solve the constructed optimization model, and two sets of comparison experiments are designed. The experimental results show that compared with the two classical algorithms, the simulated annealing-genetic hybrid algorithm (GA-SA) has better solution performance, inherits the robustness and potential parallelism of the genetic algorithm, and has a higher practical value. Meanwhile, although the total driving distance of the vehicle path considering carbon emissions increases by 3.52 km, the carbon emission cost and the total cost decrease by 5.6% and 3.4%, respectively, which confirms that the path optimization model considering carbon emissions constructed in this study can not only play the role of restraining carbon emissions but also reduce the total distribution cost and the waste of resources. In this study, a continuous function is used to represent the vehicle speed variation, and two classical optimization algorithms (the genetic algorithm and simulated annealing algorithm) are combined and parameter-optimized, and certain innovations are made in the processing of vehicle speed and the solution algorithm. Finally, the effectiveness of the model and algorithm is verified by experiments.

Analyzing travel time belief reliability in road network under uncertain random environment

The reliable operation of urban road traffic is the vision of urban development. The reliability measurement method with travel time as a performance index is the starting point of this study. Based on the travel time data, we have studied the shortcomings of existing travel time reliability indicators. Most of them simplify or even ignore the information of traffic performance thresholds. According to the characteristics of the actual urban road network, we proposed measurement of travel time reliability under uncertain random environment, that is, the travel time belief reliability. By extracting the information of traffic service subject and object, this method takes into account the influence of cognitive and random uncertainty on reliability. Next, we established the belief reliability model of travel task under the uncertain random road environment. The model considers the route selection, departure status and road conditions and gives the route selection algorithm under a time-varying road network. In addition, the impact of road objective factors and driving state factors on the travel time threshold is discussed by using the uncertainty regression analysis method. Finally, we took the actual travel task in Beijing as an example to verify the feasibility and practicability of the model and algorithm.

Time-dependent multi-depot green vehicle routing problem with time windows considering temporal-spatial distance

Reducing distribution costs is one of the effective ways for logistics enterprises to improve their core competitiveness. Aiming at the multi-depot vehicle routing problem under the time-varying road network, this paper proposes an integer programming model with the minimum total costs by comprehensively considering the fixed costs of vehicles, penalty costs on earliness and tardiness, fuel costs and the effects of vehicle speed, load and road gradient on fuel consumption. A hybrid genetic algorithm with variable neighborhood search is developed to solve the problem. In the algorithm, the temporal-spatial distance is introduced to cluster the customers to generate an initial population to improve the quality of the initial solution. Adaptive neighborhood search times strategy and simulated annealing inferior solution acceptance mechanism are used to balance the diversification and exploitation in the algorithm iteration process. Numerical results show that the model and algorithm we proposed are rather effective. The research results not only deepen and expand the vehicle routing problem (VRP) theory research, but also provide a scientific and reasonable method for logistics enterprises to make the vehicle scheduling plan.

Distribution Route Optimization for Electric Vehicles in Urban Cold Chain Logistics for Fresh Products under Time-Varying Traffic Conditions

Electric vehicles (EVs) have been widely used in urban cold chain logistic distribution and transportation of fresh products. In this paper, an electric vehicle routing problem (EVRP) model under time-varying traffic conditions is designed for planning the itinerary for fresh products in the urban cold chain. The object of the EVRP model is to minimize the total cost of logistic distribution that includes economic cost and fresh value loss cost. To reflect the real situation, the EVRP model considers several influencing factors, including time-varying road network traffic, road type, client’s time-window requirement, freshness of fresh products, and en route queuing for charging. Furthermore, to address the EVRP, an improved adaptive ant colony algorithm is designed. Simulation test results show that the proposed method can allow EVs to effectively avoid traffic congestion during the distribution process, reduce the total distribution cost, and improve the performance of the cold chain logistic distribution process for fresh products.

Optimization of transportation routing problem for fresh food in time-varying road network: Considering both food safety reliability and temperature control.

Study on fresh food safety reliability and temperature control has being a research focus in the fresh food cold distribution optimization study field. On this basis, optimization of transportation routing problem with time windows for fresh food in time-varying road network is studied by considering both economic cost and fresh food safety loss. A calculation method for path division strategy is designed. A food safety value loss measurement function, a metric function of energy and heat conversion a measure function of carbon emission rate are employed by considering time-varying vehicle speeds, fuel consumptions, cost of temperature control, the loss of food safety reliability and carbon emissions from transportation and temperature control. The fresh food cold chain distribution vehicle routing problem model with time windows in time-varying road network is formulated based on the objective of the distribution cost and food safety value loss minimization. According to the characteristics of the model, an adaptive improved ant colony algorithm is designed. Finally, the experimental data show that the model can effectively avoid the congestion period, reasonably control the refrigeration temperature, reduce the distribution cost, and improve food safety.

Route Guidance Strategies for Electric Vehicles by Considering Stochastic Charging Demands in a Time-Varying Road Network

Electric vehicles (EVs) are being increasingly adopted because of global concerns about petroleum dependence and greenhouse gas emissions. However, their limited driving range results in increased charging demands with a stochastic characteristic in real-world situations, and the charging demands should be attributed toward charging stations in time-varying road networks. To this end, this study proposes guidance strategies to provide efficient choice for charging stations and corresponding routes, and it includes the time-varying characteristic of road networks in problem formulation. Specifically, we propose two route guidance strategies from different perspectives based on the charging demand information. The first strategy focuses on the effects of the number of EVs on the charging stations’ operation, and the reachable charging stations with the fewest vehicles are selected as the heuristic suggested ones. The other strategy considers the travel cost of individual drivers and selects the charging stations nearest to the destination as heuristic suggested ones. Both strategies ensure that the selected charging stations can be reached in a time-varying road network. In addition, we carry out a simulation analysis to investigate the performance of the proposed route guidance strategies and introduce relevant insights and recommendations for the application of the strategies under various scenarios.

On-Demand Greenwave for Emergency Vehicles in a Time-Varying Road Network With Uncertainties

The response time of emergency vehicles (EV) is critical to saving lives. In this paper, we address the two challenges to the reduction of EV response time: 1) path-searching for a reliable estimated time of arrival (ETA) in ever-changing traffic conditions and 2) elastic signal preemption to reduce the negative impact on the whole traffic flow introduced by prioritizing the EVs. While traditional path-searching methods aim to minimize only the mean value of ETA, our approach minimizes a combination of its mean and variance, as we found that the variance of speed has a significant impact on path searching. The simulation shows that our approach generates paths with reliable ETA, i.e., lower variance with the same level of accuracy. Furthermore, we formulate the elastic signal preemption (ESP) as a set of quadratic programming (QP) problems to find non-intrusive signal schedules to the fast track EVs through junctions without stopping. When the ESP module was applied in the real world, results showed about 30% reduction of response time with little impact on the overall traffic conditions.

Signal setting optimization on urban road transport networks: The case of emergency evacuation

Traffic signal setting is an important element in traffic planning of urban road networks in ordinary conditions and/or in emergency conditions. As traffic signal setting affects user behaviour mainly at the path choice dimension, this dependence has to be studied by means of assignment models. We thus present a system of models and procedures for signal setting optimization with assignment of travel demand to a congested road transport network. Two interacting procedures are developed to solve the system of models: (i) an optimization procedure to obtain an optimal configuration of signal setting parameters and (ii) an assignment procedure, incorporating a path choice model with explicit path enumeration and a flow propagation model, to capture the effects of signal setting configuration on user path choice behaviour.The system of models and procedures is applied and validated on a real test site, where the transport system is in dynamic conditions due to the time-varying road network and travel demand. The test site is a portion of a small-size town where the population needs to be evacuated due to an imminent calamitous event. The results show considerable reductions in total delays on the network and evacuation times in the case of optimized signal setting parameters.

Finding a minimum cost path between a pair of nodes in a time-varying road network with a congestion charge

The minimum cost path problem in a time-varying road network is a complicated problem. The paper proposes two heuristic methods to solve the minimum cost path problem between a pair of nodes with a time-varying road network and a congestion charge. The heuristic methods are compared with an alternative exact method using real traffic information. Also, the heuristic methods are tested in a benchmark dataset and a London road network dataset. The heuristic methods can achieve good solutions in a reasonable running time.

Signal Setting Optimisation with Route Choice Behaviour and Acyclic Profiles of Vehicular Flows in Road Networks

Traffic signal setting is an important element in traffic control and in the management of urban road networks both in ordinary conditions (ex. recurrent traffic congestion) and/or in emergency conditions (ex. non-recurrent traffic congestion caused by unexpected events). The paper presents models and procedures for signal setting optimisation of signalised junctions in a congested road network. Two interacting procedures are developed to solve the system of models:•an optimisation procedure, based on a genetic algorithm, to obtain an optimal configuration of signal setting parameters and•a simulation procedure, incorporating a path choice model with explicit path enumeration and a flow propagation model, to capture effects of signal setting configuration on user path choice behaviour.The main research contributions of this work are related to: (i) the modelling of path choice behaviour in a dynamic context with vehicular flow propagation of on link and spill-back; (ii) the optimisation procedure of signal setting parameters. The system of models and procedures has been applied and validated on a real experimental test site, where the transport system is in dynamic conditions due to time-varying road network and travel demand.