Total Travel Time Of Vehicles Research Articles

Deep Reinforcement Learning Technique for Traffic Metering in Connected Urban Street Networks

Proper metering can improve traffic operations in congested urban street networks. The available approaches either (a) use macroscopic fundamental diagrams to model traffic dynamics or (b) use numerical time–space discretization of the hydrodynamic traffic flow model with high computational requirements. Therefore, they either (a) do not represent traffic dynamics accurately or (b) are not suitable for online applications. This study introduces a deep reinforcement learning (DRL) methodology to capture traffic dynamics on a micro-level scale with the capability of capturing detailed traffic dynamics with low computational time. The DRL methodology employs two neural networks that map the location of connected vehicles in a network to traffic metering signal indications and estimate the objective function of the traffic metering problem. The outputs of the neural networks are used to construct a loss function, whose optimization provides the optimal parameters for the neural networks. Because of the complexity of the loss function, the gradient descent optimization technique with Monte Carlo simulation is used to optimize the loss function. The proposed methodology was tested on a simulated case study network in Vissim software with 20 intersections. The numerical results showed that the methodology increased throughput by 41.2% and 21.3% and reduced the total travel time of vehicles by 3.4% and 15.5% compared to a no-metering strategy. Comparing the computational time of the proposed methodology with one of the existing traffic metering approaches also showed the potential of the methodology for online applications.

Cooperative control of dynamic CAV dedicated lanes and vehicle active lane changing in expressway bottleneck areas

Bottleneck areas on expressways plague the operational efficiency of entire road systems. In mixed traffic flow environments consisting of connected and autonomous vehicles (CAVs) and connected human-driven vehicles (CHVs), it is believed that road capacity can be improved to relieve traffic congestion in bottleneck areas by setting CAV dedicated lanes (CDLs) on expressways. Existing static CDL setup methods provide fixed exclusive right-of-way for CAVs but cannot accommodate dynamic changes in traffic demand. To address this issue, utilizing lane control signals and CAV active lane-changing technology, a cooperative method involving dynamic CAV dedicated lane control (CDLC) and active lane-changing control (LCC) in the bottleneck areas of expressways is proposed in this paper. First, a fundamental diagram of traffic flow with CDLs is established based on the microscopic car-following model of mixed traffic flow, which can be used to determine the conditional thresholds for setting the CDLs of multilane expressways. Second, an expressway traffic model with CDLs is established by improving the lane-level cell transmission model. Finally, cooperative control of dynamic CAV dedicated lanes and active lane changing (CDLLC) is proposed based on the model predictive control (MPC) framework for solving the congestion problem at expressway bottlenecks in a mixed driving environment. The simulation results show that the proposed CDLLC strategy can effectively reduce the total vehicle travel time and decrease the risk of congestion propagation at expressway bottlenecks when compared to the LCC-only strategy.

Proactive integrated traffic control to mitigate congestion at toll plazas

AbstractToll plazas on expressways often experience congestion owing to the imbalance between traffic demand and supply. To address this issue, this paper designs a proactive traffic control method to relieve congestion at toll plazas based on the model predictive control (MPC) that integrates the dynamic lane configuration, that is, the numbers of electronic toll collection (ETC) and manual toll collection (MTC) lanes, and the variable speed limit (VSL). The proposed method first forecasts short‐term traffic demand in toll plazas based on the long short‐term memory neural network. Then the cell transmission model (CTM) is applied to predict the traffic evolutions in the toll plaza area. At last, the MPC‐based control framework is used to optimize the numbers of ETC and MTC lanes and the variable speed limit to minimize the total vehicle travel time in a rolling horizon. Micro‐simulation tests are carried out in VISSIM to verify the effectiveness of the proposed control method. The simulation results show that the proposed control method can significantly improve the traffic performances at the toll plazas. The findings in this study can lead to field implementation of proactive control at toll plazas.

Dynamic vehicle routing problem for flexible buses considering stochastic requests

Flexible buses provide on-demand services to one or more local communities in a specific geographical area. Bus routes can be adjusted dynamically according to real-time passenger demand in a cost-effective manner. This study investigated the dynamic bus-routing problem considering stochastic future passenger demand. A two-stage stochastic programming model was formulated to minimise the total vehicle travel time cost and penalty for rejecting requests. A rolling horizon scheme was adopted to handle the dynamic changes in passenger requests and vehicle routes. A vector-similarity-based clustering and adaptive large neighbourhood searching (VSC-ALNS) algorithm was developed to solve this problem. Vehicles and passengers were matched and clustered into groups based on vector similarity, and vehicle routes were generated using an adaptive large-neighbourhood search algorithm for each cluster. The effectiveness of the proposed method was evaluated in four cases with different demand intensities using Shanghai taxi order data. The results indicate that flexible buses are more suitable for moderate demand cases, ranging from 20 to 50 requests per square kilometre per hour.

Description of multimodal transport networks by means of graph models

The main objective of organising multimodal transport is to meet the public’s demand for transport services as much as possible while making the most efficient use of rolling stock. In order to determine the efficiency of a transport system the paper proposes that it should be viewed as a collection of different local transport systems. The issue of organising multimodal transport is related to many aspects of passenger services and rail transport operations (forecasting passenger flows, developing combined train schedules, features of the TH (transfer hub) and station complexes, etc.). The article details the way multimodal passenger chains are represented in the form of graphical models. The goals of using the constructed graphs are defined: solving problems of visualisation and analysis of multimodal transport chains as well as solving the complex problem of optimising the scheduling and planning of vehicle stops at passenger transfer points. It has been shown that the main requirements for organising this type of passenger transport are to minimise the total travel time of vehicles in order to reduce management costs for operators and to maximise the number of passengers carried in order to better meet passenger demand.

Regional Boundary Control of Traffic Network Based on MFD and FR-PID

In recent years, urban traffic congestion has become more serious and the capacity of roads has declined, resulting in frequent traffic accidents. In order to effectively alleviate the traffic congestion of the regional road network, aiming at the problem of lack of accurate OD data of the road network, a regional boundary control method of the traffic network based on fuzzy RBF neural network PID (FR-PID) is proposed by combining the theory of macroscopic fundamental diagram (MFD). Firstly, based on the traffic survey, the simulation model of the study area is built, and the basic data such as the traffic flow and the time occupation rate of each road section are obtained. Secondly, the simulation data are used to test the existence of MFD in the road network, and the controlled area is defined. Then, the vehicle change model of the road network area is established. Then, in view of the problem of poor adaptive ability of traditional PID control, the FR-PID control structure is designed. Finally, an example is verified by VISSIM software. In the simulation, different control methods are used for comparison and verification, and the simulation results are analyzed. The results show that the control effect of the proposed method is better than that of the traditional method, and the regional average accumulative vehicle number, regional average completed volume, regional accumulative delays, and total vehicle travel time are optimized by 28.21%, 41.19%, 27.06%, and 32.73%, respectively. The research results can provide reference for the management of urban congestion, thereby reducing the number of traffic accidents and improving urban traffic safety.

Traffic Signal Control With Reinforcement Learning Based on Region-Aware Cooperative Strategy

With the increase of private cars, traditional traffic signal control methods cannot alleviate the traffic congestion problem. Reinforcement learning (RL) is increasingly used in adaptive traffic light control. As urban traffic becomes more complex, reinforcement learning algorithms solely based on value or policy are not suitable for such scenarios. Moreover, the centralized method does not show a good effect on the multi-intersection, in particular, when the traffic flow is in the high-dimensional continuous state space. In this article, we propose a decentralized framework based on the advantage actor-critic (A2C) algorithm by assigning global control to each local RL agent or intersection. A2C algorithm involved in this article is a product of combining policy function and value function, which has good convergence ability and can be applied to continuous state space. The decentralized methods may put a new challenge: from the perspective of each local agent, the environment becomes partially observable. We overcome this problem by putting forward a region-aware cooperative strategy (RACS) based on graph attention network (GAT), which can incorporate the spatial information of the surrounding agents. We carry out experiments on synthetic traffic grid and real-world traffic network of Monaco city to compare with the existing A2C and Q-learning algorithms. Experimental results confirm that our RACS method has a shorter queue length and less waiting time than the two existing algorithms, and can reduce the total vehicle travel time.

Clustered tabu search optimization for reservation-based shared autonomous vehicles

ABSTRACT This paper investigates the optimization of Reservation-based Autonomous Car Sharing (RACS) systems, aiming at minimizing the total vehicle travel time and customer waiting time. Thus, the RACS system and its routing are formulated with a consideration for system efficiency and passengers’ concerns. A meta-heuristic Tabu search method is investigated as a solution approach, in combination with K–Means (KMN–Tabu) or K–Medoids (KMD–Tabu) clustering algorithms. The proposed solution algorithms are tested in two different networks of varying complexity, and the performance of the algorithms is evaluated. The evaluation results show that the TS method is more suitable for small-scale problems, while KMD–Tabu is suitable for large-scale problems. However, KMN-Tabu has the least computation time, although the solution quality is lower.

Genetic algorithm-based simulation optimization of the ALINEA ramp metering system: a case study in Atlanta

ABSTRACT This paper presents a case study of the optimal ALINEA ramp metering system model of a corridor of the metro Atlanta freeway. Based on real-world traffic data, this study estimates the origin-destination matrix for the corridor. Using a stochastic simulation-based optimization framework that combines a micro-simulation model and a genetic algorithm-based optimization module, we determine the optimal parameter values of a combined ALINEA ramp metering system with a queue flush system that minimizes total vehicle travel time. We found that the performance of ramp metering with optimized parameters, which is very sensitive possibly because bottlenecks are correlated, outperforms the no control model with its optimized parameters in terms of reducing total travel time.

A restricted path-based ridesharing user equilibrium

In this paper, a restricted path-based ridesharing user equilibrium (RUE) is proposed to build up the rationales between ridesharing activities and traffic congestion. In the traffic assignment problem with ridesharing, travelers simultaneously choose routes from origins to destinations and travel modes (including solo driver, ridesharing driver, and ridesharing passenger). The proposed RUE model with nonadditive path costs is more realistic than the existing ones based on some additional model specifications: ridesharing drivers bear an extra cost for taking riders and meanwhile receive an extra subsidy, and each rider is carried by only one driver. The Braess paradox is examined within the context of the proposed RUE model. The numerical results on the Braess network with and without high-occupancy toll (HOT) lane show that (a) the existence of the HOT lane will facilitate more travelers’ sharing rides, reduce the number of total vehicles (drivers), and improve the total vehicle travel time (TVTT); (b) the toll charge of solo drivers on HOT lane will promote the ridesharing activities while this effect is conditional on the levels of the extra cost and subsidy. All numerical results on the Braess network and the grid network show that more travelers will participate in ridesharing when the amount of extra subsidy for ridesharing drivers increases.

Quantifying the impact of road capacity loss on urban electrified transportation networks: An optimization based approach

Abstract The proliferation of electric vehicles and commercialization of public charging facilities have inspired the emerging trend of transportation electrification, which creates an urgent demand for systematic methodologies to analyze the performance of electrified transportation networks (ETNs) while taking the interdependency across the transportation network and the power distribution network into account. This paper introduces a comprehensive mathematical formulation of the ETN from a system-level perspective, in which the steady-state distribution of the traffic flow is described by Wardrop user equilibrium, while the operating status of the power grid is characterized by a linearized branch power flow model. Moreover, this paper investigates the impact of road capacity degradation on the transportation network, which is quantified by the total vehicle travel time, as well as the power distribution network, which is quantified by the operating cost. The former gives rise to a nonlinear programming problem with complementarity constraints (NPCC), which is reformulated as a mixed integer linear program, thereby the global optimal solution can be found with moderate computational effort. The latter yields a max–min NPCC, which is transformed into an equivalent single-level NPCC; thus a commercial nonlinear solver is applicable to find a local solution. A derivative-free searching algorithm which relies on convex optimization is also suggested. Numerical case studies on a test system demonstrate the necessity of considering the intrinsic interdependency of the transportation and electricity infrastructures in designing and operating the ETN.

Influence of meta-heuristic optimization on the performance of adaptive interval type2-fuzzy traffic signal controllers

Intelligent traffic control systems optimized using meta-heuristic algorithms can greatly alleviate traffic congestions in urban areas. Meta-heuristics are broadly used as efficient approaches for complex optimization problems. Comparing the performance of optimization methods on different applications is a way to evaluate their effectiveness. The current literature lacks studies on how performance of traffic signal controllers is affected by utilized optimization algorithms. This paper evaluates the performance of three meta-heuristic optimization methods on an advanced interval type-2 adaptive neuro-fuzzy inference system (IT2ANFIS)-based controller for complex road networks. Simulated annealing (SA), genetic algorithm (GA), and the cuckoo search (CS) are applied for optimal tuning of IT2ANFIS controller. Optimizations methods adjust the parameters in a way to reduce the total travel time of vehicles in the road network. Paramics is used to design and simulate urban traffic network models and implement proposed timing controllers. Comprehensive simulation and performance evaluation are done for both single and multi-intersection traffic networks. Obtained results reveal significant superiority of IT2ANFIS trained using CS method over other controllers. The average performance of the CS-IT2ANFIS is about 31% better than the benchmark fixed-time controllers. This is 17% and only 3% for GA-IT2ANFIS and SA-IT2ANFIS controllers respectively.

Space Optimization of Speed Guidance Control on Urban Expressway

Urban expressway plays an important role in the urban road network. It is gradually shifting from large-scale infrastructure-oriented to refinement of traffic management. In order to improve the efficiency and safety of urban expressway, macroscopic dynamic traffic flow model is extended under speed guidance control. Multi-objective optimization model is established based on the total travel time of vehicles and the value of speed guidance changes. Three scenarios are carried out with Strength Pareto Evolutionary Algorithm. The optimal scenario is adopted based on set theory then the best solution is determined. Simulation analysis is carried out in simulation platform under the optimal solution. The results show that speed guidance control has a good effect to smooth traffic flow, alleviate traffic congestion and improve traffic safety which can be applied in practice to provide a theoretical support for active traffic management.

Integrated Approach Combining Ramp Metering and Variable Speed Limits to Improve Motorway Performance

Ramp metering (RM) and variable speed limits (VSLs) are two widely used intelligent transportation system (ITS) means to improve and manage motorway traffic. The former controls the flow of traffic into motorways from on-ramps, and the latter affects the speed of traffic on the motorway main line. An integrated approach to the prudent use of these two ITS measures can help to achieve optimal utilization of motorways. This study proposed a new method to integrate RM with VSL controllers to attain an efficient and equitable motorway system. The proposed method was used to combine a local and a coordinated RM strategy, namely ALINEA and Heuristic Ramp Metering Coordination (HERO), with VSLs. The method developed was assessed with a case study in Auckland, New Zealand. The analysis of the case study was based on a critical bottleneck section of Auckland Motorway by using the AIMSUN microsimulator. The outcome performance was assessed in relation to the efficiency and equity of the motorway system. The efficiency was measured by total vehicle travel time, average number and duration of stops, and emission levels; the equity of the motorway system was measured by the Gini coefficient. The main results were that the modified VSLs and the HERO + VSL control scenarios outperformed all other control scenarios in improving vehicular emissions, total travel time, and the equity measure, respectively. This outcome provides prospects for the developed method.

Mixed manual/semi-automated traffic: a macroscopic analysis

The use of advanced technologies and intelligence in vehicles and infrastructure could make the current highway transportation system much more efficient. Semi-automated vehicles with the capability of automatically following a vehicle in front as long as it is in the same lane and in the vicinity of the forward looking ranging sensor are expected to be deployed in the near future. Their penetration into the current manual traffic will give rise to mixed manual/semi-automated traffic. In this paper, we analyze the fundamental flow–density curve for mixed traffic using flow–density curves for 100% manual and 100% semi-automated traffic. Assuming that semi-automated vehicles use a time headway smaller than today’s manual traffic average due to the use of sensors and actuators, we have shown using the flow–density diagram that the traffic flow rate will increase in mixed traffic. We have also shown that the flow–density curve for mixed traffic is restricted between the flow–density curves for 100% manual and 100% semi-automated traffic. We have presented in a graphical way that the presence of semi-automated vehicles in mixed traffic propagates a shock wave faster than in manual traffic. We have demonstrated that the presence of semi-automated vehicles does not change the total travel time of vehicles in mixed traffic. Though we observed that with 50% semi-automated vehicles a vehicle travels 10.6% more distance than a vehicle in manual traffic for the same time horizon and starting at approximately the same position, this increase is marginal and is within the modeling error. Lastly, we have shown that when shock waves on the highway produce stop-and-go traffic, the average delay experienced by vehicles at standstill is lower in mixed traffic than in manual traffic, while the average number of vehicles at standstill remains unchanged.

A hybrid genetic algorithm for the vehicle scheduling problem with due times and time deadlines

In this paper, I propose a hybrid genetic algorithm (HGAV) incorporating a greedy interchange local optimization algorithm for the vehicle scheduling problem with service due times and time deadlines where three conflicting objectives of the minimization of total vehicle travel time, total weighted tardiness, and fleet size are explicitly treated. The vehicles are allowed to visit the nodes exceeding their service due times with a penalty, but within their latest allowable times. The HGAV employs a mixed farming and migration strategy along with a variant of partially mapped crossover and several mutation operators newly developed while maintaining the solution feasibility during the whole evolutionary process. The HGAV is extensively evaluated on the various types of test problems, including a sensitivity analysis with varied genetic parameters on the weighted sum of three objectives of a solution. The results show that the HGAV always attains better solutions than the BC-saving algorithm, but with a much longer computation time.

A heuristic for bi-objective vehicle routing with time window constraints

This paper is concerned with the bi-objective vehicle routing problem with time window constraints (BVRPTW). The BVRPTW is to determine the most favorable vehicle routes that minimize the total vehicle travel time and the total customer wait time which are, more often than not, conflicting. We construct a linear goal programming (GP) model for the BVRPTW and propose a heuristic algorithm to relieve a computational burden inherent to the application of the GP model. The heuristic algorithm consists of a parallel insertion method for clustering and a sequential linear goal programming procedure for routing. The results of computational experiments showed that the proposed algorithm finds successfully the more favorable solutions in most cases than the Potvin an Rousseau's method that is known as a very good heuristic for the VRPs with time window constraints. The proposed algorithm was capable of generating a numerous nondominated solutions through the change of the target values of the two objectives and the decision maker's preference on the objectives expressed as α.