Vehicle Travel Time Research Articles

Efficiency of Vehicle Actuated traffic control on isolated intersection

This paper is described different strategies for traffic control and simulation, namely - Vehicle Actuated (VA) compared to Fixed Time (FT) traffic control. We are comparing and evaluating the impact of FT and adaptive traffic signal control strategies for one of the biggest and busiest intersections in Tashkent, the capital of Uzbekistan. The parameters used for comparing these strategies are the average vehicle travel times and average vehicle queue lengths. TRL TRANSYT 15 software was used to determine the fixed signal timing plan based on intersection geometry, inter-green matrix, and traffic flow. PTV VISSIM software is used for VA algorithm development and simulation to get the maximum and average vehicle travel times as well as queue lengths. It is shown that the parameter optimization can reduce the queue lengths down to 65% and the travel time down to 51%.

Intelligent algorithm-based efficient planning of electric vehicle charging station: A case study of metropolitan city of India

Electric vehicles are gaining popularity and going to become the mainstream mode of transportation in urban and rural areas not only in India but globally in the next few years. In the adoption of electric vehicles, there are certain issues like proper charging infrastructure, charging time etc. and out of these the sizing and siting of the charging station particularly in urban areas where the cost of land and location plays an important role. Thus, it is important that the charging station location should be easily accessible for the electric vehicle users and cost effective as well. This paper presents an intelligent algorithm based efficient planning of electric vehicle charging station considering geographical information and road network. The cost function has considered such as investment cost, charging station electrification cost, electric vehicle energy loss cost and travel time cost. An intelligent algorithm-based approach is employed to solve the planning problem of electric vehicle charging stations. Further, the impact on reliability of the grid is also evaluated by determining the charging cost loss on each considered location. The result shows that the applied method provides better optimized solutions which are beneficial for electric vehicles users, charging station operator and utility grid.

Research on Truck Lane Management Strategies for Platooning Speed Optimization and Control on Multi-Lane Highways

Automated truck platooning has become an increasingly popular research subject, and its applicability to highways is considered one of the earliest possible landing scenarios for automated driving. However, there is a lack of research regarding the combination of truck platooning technology and truck lane management strategy on multilane highways in the environment of a cooperative vehicle–infrastructure system (CVIS). For highway weaving sections under the CVIS environment, this paper proposes a truck platooning optimal speed control model based on multi-objective optimization. Through a combination of model predictive control and the cell transmission model, this approach considers the bottleneck cell traffic flow, overall vehicle travel time, and truck platooning fuel consumption as objectives. By conducting experiments on a mixed traffic flow simulation platform, the multi-lane management strategies and optimal speed control effect were evaluated through different scenarios. This study also determined the appropriate proportion of truck platooning for an exclusive lane and to increase truck lanes, thus providing effective lane management decision support for highway managers.

Measuring Traffic Congestion with Novel Metrics: A Case Study of Six U.S. Metropolitan Areas

Quantifying traffic congestion is a critical task for transportation planning and research. Numerous metrics have been developed, mainly focusing on changes in vehicle speeds, their extents, and travel time. In this study, new metrics are presented using the Hägerstrand’s space-time cube that has been studied from time geography perspectives since the 1960s. Particularly, the product of distance and time, i.e., distanceTime, is proposed as a base metric to measure traffic congestion amounts. Using the base metric such as mileHours, metrics of weighted congestion and normalized congestion amounts were also developed. New metrics were applied to six metropolitan areas and their vicinities in the United States (Atlanta, Chicago, Washington, D.C. and Baltimore, Dallas and Fort Worth, Los Angeles, and New York), and congestion amounts were calculated and compared. The Google Traffic Layer API was used to obtain traffic congestion datasets for six months (April–September 2022), and GIS (geographic information systems) was used for delineating road features and traffic intensity levels. Among the six areas, New York and its vicinity showed the largest congestion when only heavy congestion was used. Los Angeles and its vicinity showed the largest congestion when all congestion levels were considered. This study shows that the proposed metrics are very effective in summarizing traffic amounts and broadly applicable for further analyses of traffic congestion phenomena by associating various other factors, such as weekdays, months, or gas prices. The new metrics developed in this research may help transportation researchers and practitioners by providing them with a set of metrics applicable to summarizing congestion amounts by synthesizing congestion intensity, extent, and duration.

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.

Development prediction algorithm of vehicle travel time based traffic data

Development prediction algorithm of vehicle travel time based traffic data

Modeling the performance of restricted crossing U-turn intersections including the effects of connected and autonomous vehicles: a case study in California

Despite numerous studies demonstrating the effectiveness of restricted crossing U-turn (RCUT) intersection design, its implementation remains close to zero in some large states (e.g., California). The study included four locations on high-speed rural expressways (highways with partial access control) in California. The operational evaluation relies on microscopic simulation models of existing two-way stop-controlled (TWSC) intersections and alternate RCUT designs used to estimate network-wide performance measures. Two series of simulation scenarios were tested: (1) scenarios with no connected and autonomous vehicles (CAVs) and (2) scenarios with different SAE International Level 4 CAV market penetration rates (MPRs) to help policymakers and practitioners in future planning strategies. The microsimulation models were also used to generate trajectory data to create a surrogate measure-based approach. Based on the results, the RCUT designs reduced or eliminated the more severe crossing conflicts. Similar vehicle travel times were identified in various CAV MPRs except for one of the locations with challenging geometric features, where travel time increased at higher CAV MPRs.

Approaches to Managing Speed in New Zealand’s Capital

Higher speeds increase the risk of crashes occurring and the severity of resulting injuries. The purpose of speed management is to ensure that speeds are safe for all road users thereby reducing the number of crashes and avoid injuries and deaths when crashes happen. In response to a rising trend in deaths from road crashes in recent years, the New Zealand Government introduced the Road to Zero road safety strategy in 2018 and updated the Land Transport Rule: Setting of Speed Limits in 2022. In Wellington city (New Zealand’s capital), unsafe speed limits and high numbers of road crashes with injuries make city streets unsafe, have high social costs, present a barrier to active transport, and limit mode choice for residents and visitors. This study assessed nine speed management options using cost benefit analysis. Citywide permanent speed reduction was most effective for reducing road crash-related injuries. The option with 30 km/h speed limit for local streets and 40 km/h for arterial streets had the highest crash reduction benefits of over NZD $500 million, discounted over 40 years followed by the option of 30 km/h default citywide, albeit with the high construction costs and relatively high vehicle travel time disbenefits. Incremental cost benefit calculations indicated that, depending on the total budget available, the most efficient options were permanent speed reductions near schools and the 30/40 km/h mix citywide. Implementing variable speed limits around schools provided the lowest benefits and very low value for money compared to citywide speed management approaches or permanently reduced speed around schools.

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.

Probability and number of bruises in bovine carcasses according to animal type, handling and transport.

The aim of this study was to evaluate pre-slaughter risk factors on the probability of bruising and the number of bruises in different regions of the bovine carcass. The study evaluated the data from 4061 batches of slaughtered cattle, totalling 199,026 carcasses, from Hereford and Aberdeen Angus animals in crosses with continental European breeds and Zebu cattle. The factors that caused injury were sex, handling conditions (score), vehicle type, vehicle load density (kg/m2), travel time (minutes), travel distance (kilometres), physiological maturity (dentition), animal reactivity (score), and carcass fat (score). The type of vehicle had the greatest influence on the probability of carcass bruising and affected all carcass cuts. More bruising occurred in vehicles with a greater load capacity. A higher chance of bruising was seen in groups of older animals, those with an excitable temperament and, especially, in groups of females. Higher load densities during transport resulted in a greater chance of bruising occurring in all cuts except the hindquarters. Distance and travel time are unstable in the models, but also detrimental, increasing the probability of bruising and the number of bruises in the various cuts. The results of this study suggest that good practices in the welfare, handling and transport of the animals, as well as training the personnel responsible for their handling, should be adopted to minimise the risk of injury at different locations on the carcass.

Model development of road performance indicator-related travel time using international roughness index: A case study national road network of Sulawesi

The International Roughness Index (IRI) is widespread in measuring road performance. However, travel time is a more critical benchmark in measuring road performance and the transportation system's performance. Therefore, new studies and models are needed to determine the relationship between IRI and travel time to obtain a segment performance function model influenced by road conditions. The study begins with a traffic condition survey to obtain vehicle travel time data on various road conditions. Assessment of road conditions uses the value of road surface flatness obtained from the results of Hawkeye measurements by carrying out actual travel throughout the national road network of Sulawesi Island. The study results show that the IRI value greatly influences the achievement of travel time as a road performance indicator in Indonesia. The equation of the relationship function of the vehicle travel time in free flow with the value of the flatness of the road surface is y=−6,062x2 +1,068x−1,0873 with R2 0,9314. At the same time, the equation of travel time as a function of road performance is ta = α1 (v/c)β + α1 (v/c) - 6,062x2 + 1,068x - 1,0873.

Cycle-Based Estimation on Lane-Level Queue Length at Isolated Signalized Intersection Using License Plate Recognition Data

Queue length is an important parameter to evaluate the congestion level of urban intersections. Abundant methods based on different data sources, such as loop detector data and mobile sensor data, for estimating queue length have received wide attention. A license plate recognition (LPR) system recording vehicle information can be used as a data source to estimate lane-level queue length. In this study, an improved method is proposed for cycle-based queue length estimation at an isolated signalized intersection using LPR data, considering the residual queued vehicles under oversaturated conditions. First, a modified interpolation method is developed to infer the travel time of unmatched vehicles. Then, the complete arrival and departure information are processed as three characteristic parameters of vehicles, which are the input to the maximum probability function for queue length estimation at each lane. Finally, the proposed model is validated using actual cycle maximum queue length collected from a video camera and LPR data in Changsha city, China. The numerical results showed that the proposed model can achieve accurate estimation for lane-level queue length.

Optimization of Safety Facilities for Collision Analysis between Motor and Non-Motor Vehicles at Intersections

With the increasing number of the motor and non-motor vehicles, conflicts and safety risks between motor vehicles and non-motor vehicles are increasing. Among them, the collision between right-turning vehicles and non-motor vehicles going straight leads to traffic accidents accounting for up to 50% of the non-accident cases at intersections. The intersection of Erdos East Street and Xing an’ South Road in Hohhot City is selected as the research object. Analyze the conflict between right-turning motor vehicles and non-motor vehicles. The optimized design was carried out from three perspectives: right-turn channelization island design, road deceleration facilities layout, and signal control, and the road network model was built with VISSIM survey data to demonstrate and evaluate the scheme. The results show that when the traffic volume is unchanged, the number of non-collision decreases and the passage time of non-motor vehicles decreases by 4%. The travel time for left-turn vehicles also dropped 16 percent. It shows that the collision between motor vehicles and non-motor vehicles at right-turn intersections is significantly improved through the above optimal design scheme.

Reducing Waiting Times at Charging Stations With Adaptive Electric Vehicle Route Planning

Electric vehicles are becoming more popular all over the world. With increasing battery capacities and a growing fast-charging infrastructure, they are becoming suitable for long distance travel. However, queues at charging stations could lead to long waiting times, making efficient route planning even more important. In general, optimal multi-objective route planning is extremely computationally expensive. We propose an adaptive charging and routing strategy, which considers driving, waiting, and charging time. For this, we developed a multi-criterion shortest-path search algorithm using contraction hierarchies. To further reduce the computational effort, we precompute shortest-path trees between the known locations of the charging stations. We propose a central charging station database (CSDB) that helps estimating waiting times at charging stations ahead of time. This enables our adaptive charging and routing strategy to reduce these waiting times. In an extensive set of simulation experiments, we demonstrate the advantages of our concept, which reduces average waiting times at charging stations by up to 97 %. Even if only a subset of the cars uses the CSDB approach, we can substantially reduce waiting times and thereby the total travel time of electric vehicles.

Разработка имитационной дискретно-событийной модели транспортной инфраструктуры с использованием инструментов оптимизации

The paper describes a discrete-event simulation model of transport and logistics infrastructure on the example of a road intersection at Profsoyuznaya Street, 64 k.2 / Nametkina Street, 6. The model allows the studying of traffic processes at the intersection with a high degree of accuracy, drawing analytical conclusions and making correct management decisions to improve the traffic situation on the basis of these conclusions. The developed simulation model consists of road network elements, agent generation system, process diagram functional blocks, control elements and infographics, etc. A number of simple experiments were performed with the model, including optimization, as a result of which the most optimal duration of the traffic light phases was determined, which ensures the minimum average travel time of vehicles. In addition, the model includes ground crossings that allow the study of the behavior of pedestrian masses when crossing the carriageway. The proposed solutions to the organization of traffic and pedestrian flows can be used to implement simulation models for a wide range of issues of modeling transport and logistics infrastructure, including railway transport, for example, when developing simulation models of transport interchange nodes. Software that was used in the study: the simulation environment AnyLogic 8 and the object-oriented programming language Java. The conducted research and preparation of the publication were carried out as part of the implementation of a grant from Russian Railways for the development of scientific and pedagogical schools in the field of railway transport.

Estimation of Vehicle Energy Consumption at Intersections Using Microscopic Traffic Models

This paper addresses issues related to modeling energy consumption and emissions using microscopic traffic simulations. This paper develops a method in which a traffic model is used to calculate the energy needed to travel through selected types of intersections. This paper focuses on energy consumption and derived values of calculated energy, which can be, for example, carbon dioxide emissions. The authors present a review of the scientific literature on the study of factors affecting energy consumption and emissions and methods to estimate them in traffic. The authors implemented an energy consumption model into a microsimulation software module to estimate results as a function of varying traffic volumes at selected types of intersections and for selected traffic organization scenarios. The results of the study show the lowest energy consumption and the lowest emissions when road solutions are selected that contribute to reducing vehicle travel times on the urban street network at higher average vehicle speeds. In addition, the positive impact of the share of electric vehicles in the traffic flow on the reduction of energy consumption and emissivity was estimated.

Pre-trip reservation enabled route guidance and signal control cooperative method for improving network throughput

With the ever-increasing traffic demand, efficient urban traffic operation is facing more and more pressure and challenges. Route guidance and signal control are effective methods to enhance the existing urban traffic capacity and realize the sustainable development of traffic systems. In this paper, a collaborative optimization method of route guidance and intersection signal controlling is proposed under the background of a pre-trip reservation. Vehicles were divided into reserved and non-reserved vehicles, which corresponds to pre-trip reservation and non-reserved travel, respectively. Firstly, route guidance models considering both road network efficiency and vehicle travel time are proposed for different types of vehicles. Then, based on the impact of different travel modes on traffic flow distribution, a collaborative optimization for route guidance and intersection signal control was realized through the proposed dynamic signal optimization method. The experimental results showed that road network capacity was improved by 16% under the condition of high traffic flow by using the proposed optimization method. The proposed method can also alleviate the congestion of some sections without causing the transfer of congestion. The effectiveness of the proposed method was verified by simulation experiments, which can provide a theoretical reference for traffic control and management in the context of pre-trip reservation.

Deep Reinforcement Learning for Traffic Light Timing Optimization

Existing inflexible and ineffective traffic light control at a key intersection can often lead to traffic congestion due to the complexity of traffic dynamics, how to find the optimal traffic light timing strategy is a significant challenge. This paper proposes a traffic light timing optimization method based on double dueling deep Q-network, MaxPressure, and Self-organizing traffic lights (SOTL), namely EP-D3QN, which controls traffic flows by dynamically adjusting the duration of traffic lights in a cycle, whether the phase is switched based on the rules we set in advance and the pressure of the lane. In EP-D3QN, each intersection corresponds to an agent, and the road entering the intersection is divided into grids, each grid stores the speed and position of a car, thus forming the vehicle information matrix, and as the state of the agent. The action of the agent is a set of traffic light phase in a signal cycle, which has four values. The effective duration of the traffic lights is 0–60 s, and the traffic light phases switching depends on its press and the rules we set. The reward of the agent is the difference between the sum of the accumulated waiting time of all vehicles in two consecutive signal cycles. The SUMO is used to simulate two traffic scenarios. We selected two types of evaluation indicators and compared four methods to verify the effectiveness of EP-D3QN. The experimental results show that EP-D3QN has superior performance in light and heavy traffic flow scenarios, which can reduce the waiting time and travel time of vehicles, and improve the traffic efficiency of an intersection.

Mapping high-resolution urban road carbon and pollutant emissions using travel demand data

Attributed to the residents’ increasing concern on the environment and urban life, urban road carbon and pollutant emissions have been receiving much attention recently. However, because of the diversity of urban road emission sources and the complexity of network topological structure, urban road emissions are hard to be quantitatively measured. This study provides a method that incorporating a traffic assignment problem that uses travel demand data and outputs traffic flow and average speed of different types of vehicles on the road. By using the proposed method, high-resolution, covering temporal and spatial resolution, emission resolution, and vehicle category resolution, vehicle carbon and pollutant emissions can be figured out. The proposed method can be extended to many regions and cities by using corresponding travel demand data. The travel demand data of the largest city in the U.S. state of South Dakota was used to test the method and to figure out the emissions. Numerical analyses were conducted to provide practical insights into traffic emission management, which indicate that network-level road emissions are strongly related to vehicle travel time and travel demand and that mitigating traffic congestion of the urban road network is an effective way to reduce the network-level emissions.

An Integrated Model of Continuous Review Inventory and Vehicle Routing Problem with Time Windows

Item stock-out or shortage is a great issue for customer satisfaction and can be countered by providing safety stocks. This study discusses an authorized distributor of smartphone and tablet computer products in Indonesia. Currently, the company is considering a plan to take over the role of a logistics service company to reduce the total logistics costs that must be incurred by making improvements in terms of inventory management. Inventory management can also protect the company from the impact of inflation and price increases that will impose logistics costs. This study has assumed that the vehicle travel speed or travel time between two nodes is fixed. This study contributes to minimizing total logistics costs by integrating the concept of a continuous review lot size-reorder point (Q, R) inventory model with lost sales and vehicle routing problems with time windows (VRPTW). Continuous review (Q, R) inventory model with lost sales shows the retailers’ cycle demand ranges from 130 to 234 units. This condition leads to the value of the expected average annual inventory cost IDR 27.263.204.625,59. Based on the VRPTW calculation, the total distribution cost per trip is IDR 445.631.642,7. Based on the current total distribution costs, it was found that the total distribution costs were IDR 849.454.616,2, so there is a decrease in distribution costs of 47.54%. The total logistics cost that must be incurred by the company to deliver smartphone products to 10 retailers in Jakarta and its surrounding areas is IDR 27,708,836,268.29.