Vehicle Travel Time Research Articles

Travel Time Prediction on Long-Distance Road Segments in Thailand

This study proposes a method by which to predict the travel time of vehicles on long-distance road segments in Thailand. We adopted the Self-Attention Long Short-Term Memory (SA-LSTM) model with a Butterworth low-pass filter to predict the travel time on each road segment using historical data from the Global Positioning System (GPS) tracking of trucks in Thailand. As a result, our prediction method gave a Mean Absolute Error (MAE) of 12.15 min per 100 km, whereas the MAE of the baseline was 27.12 min. As we can estimate the travel time of vehicles with a lower error, our method is an effective way to shape a data-driven smart city in terms of predictive mobility.

Capability of Intermittent Bus Lane Utilization for Regular Vehicles

Intermittent bus lanes (IBLs) can improve road capacity by allowing other regular vehicles to drive in the idle space of a dedicated bus lane. However, excessive vehicles in the IBL will cause additional bus delays. To avoid such problems, this study proposes a method to determine the capability of IBL permitted for regular vehicles first, and then use it as the total amount restriction of lane-borrowing vehicles to implement a bus lane control strategy that will improve road capacity and avoid additional bus delays. A model for calculating the capability of IBL is also provided. Vehicles between two buses are designated as potentially lane-borrowing vehicles that could follow the buses to leave the road section. The evolution process of these vehicles in the unit is analyzed using kinematic wave theory to obtain the formed traffic queue length. Using the rear bus trajectory to set the length limit on the traffic queue, the estimated total amount of lane-borrowing vehicles is corrected to establish the final capability of the IBL. The applicability of the method was evaluated from three perspectives: bus departure interval, road traffic saturation, and near-side bus stop. The simulation results showed that the proposed method can guarantee no additional bus delay compared to the situation of a dedicated bus lane. It can also improve road capacity more than traditional IBL under any degree of saturation and bus departure interval. Compared with traditional IBL, the average travel time of regular vehicles is shorter, except when the degree of saturation is high and the bus departure interval is large.

Multiobjective Green Time-Dependent Location-Routing Problem and Algorithms

To reduce the logistic cost and carbon emission and improve customer satisfaction, this study proposes a multiobjective green time-dependent location routing problem (MOGTDLRP) model in which the objectives are to minimize the distribution total cost, delivery time, and fuel consumption. This model will be solved by several hyperheuristic algorithms which include the high-level heuristics and the low-level heuristics. There are three acceptance criterions for the solution: improving and equal, all moves and accept all solutions, and dynamic acceptance criteria. Through the case, the performance of the algorithm and the influence of various factors on the solution are analyzed in this study. The experimental results show that the proposed model can effectively reduce logistic costs, carbon emissions, and vehicle travel time.

Comparative Analysis on Road Users’ Cost Using HDM-4 Software and Manual Technique: A Case of Addis Ababa-Adama Expressway

Continuous changes in vehicle technology, road condition, traffic compositions initiate the change or updating of road users’ cost models. So that it needs to practice a continuous revision or update periodically for realistic estimation of costs and benefits. This paper presented the relationship and comparison between road users’ cost along Addis Ababa-Adama newly constructed expressway using the Highway Development and Management (HDM-4) Software and manually using formulations developed in the Portuguese model. The method started with data collection. All input data were collected from primary and secondary sources. The primary data utilized an interview, and secondary data were sourced out from pertinent documents, both published and unpublished. More data were gathered that related to vehicles. The vehicles using the road are classified based on the manual from the Ethiopian Road Authority as cars, utilities, small bus, large bus, small truck, medium truck, heavy truck, and truck trailer. The collected data have been input into the HDM-4 interface; the output of the analysis was vehicle operating costs, travel time, and road users’ cost as a summation. Using manual technique and HDM-4 Software, Birr 128.62/km/vehicle and Birr 139.23/km/vehicle, respectively, were found from road users’ costs analysis. The result shows the difference of Birr 10.61. Also, the correlation coefficient of 0.75 is determined, which shows that the two results of road users’ costs are highly related. As a result, the study reveals that the application of HDM-4 Software and the manual technique formulations from the Portuguese model can be adopted interchangeably to calculate Road Users’ Cost of road sections in Ethiopia. Hence, the study results are expected to be an eye-opener for a future similar project by the concerned agencies.

Analisis Kerusakan Jalan Perkerasan Lentur menggunakan Metode Pavement Condition Index (PCI)

Overall condition of the East Ring Road I, Kec. Paalmerah Jambi City, which suffered light, moderate, or heavy damage in several segments, the East Ring Road I, Kec. Paalmerah Jambi City is a primary arterial road with national road status and based on its location in an urban 2/2 road type, and also the East Ring Road I is a connecting road to the Talang Duku port where vehicles pass through the road with or without load, the East Ring Road I there are several offices and warehouses, you could say the area is an industrial area. on these roads there is damage that interferes with the comfort of drivers where damage to this segment can interfere with the activities of road users so that it can affect vehicle travel time to be slow. on these roads there is damage that interferes with the comfort of drivers where damage to this segment can interfere with the activities of road users so that it can affect vehicle travel time to be slow. Within a few years of being born there is quite a lot of damage at some point, Type of Damage. As a result disturbing comfort in driving and even accidents . The results of the analysis of road damage using the PCI method obtained a PCI value of 67,333 with good pavement conditions. Based on the graph of the relationship between the level of road damage and speed, it can be concluded that the higher the level of damage, the lower the PCI value and will affect the slow speed of the vehicle. On the other hand, the higher the PCI value, the lower the damage rate and the faster the vehicle speed.

Mapping Bus and Stream Travel Time Using Machine Learning Approaches

Collection of travel time data has always been a strenuous task, especially on Indian roads, due to the highly mixed traffic conditions and the absence of rigid driving characteristics. Travel time data collection methods such as on-board GPS devices and Wi-Fi scanners have their own feasibility issues. The GPS devices cannot be installed on all private vehicles, and Wi-Fi scanners cannot be set up at each and every road corridor. However, many transit agencies of Indian cities are installing GPS units in their public transport buses, making them a rich source of data. These buses travel all over the network and record the travel time and location information of vehicles at a certain interval, which can become the best and reliable source of traffic data under such conditions. The only limitation with this data is that the buses differ greatly from the rest of the vehicles in terms of vehicle characteristics, freedom of driving, effect of bus-stops, etc. So, a viable solution is to model and link the stream and bus travel times. Based on this, the present study proposes a method to map bus travel times with stream travel times using machine learning techniques, namely, Gradient Boosting Method and Support Vector Machines. Preliminary analysis showed that a nonlinear relationship exists between the travel times, and other factors like peak/off-peak hours, day of the week, etc. Results show that the proposed methods can efficiently map bus travel times with stream travel times with an average MAPE of 20% and perform better than existing approaches such as linear regression and artificial neural networks.

Evaluasi Kinerja Simpang Tiga Tak Bersinyal Jl. Muharto - Jl. Mayjen Sungkono – Jl. Raya Ki Ageng Gribig Kota Malang

The unsignal intersection is the most common type of intersection found in urban areas, one of which is the unsignalized three-way intersection Muharto St. – Mayjen Sungkono St. – Raya Ki Ageng Gribig St, Malang City. Transportation problems that often occur include traffic jams and delays in travel time. Vehicle travel time as one of the service performance criteria at the intersection. These problems are often found in several areas in Indonesia, including at the intersection of Muharto St. – Mayjen Sungkono St. – Raya Ki Ageng Gribig St. is located in Malang city, which is the main route for large market vehicles to pass and is the road to access the center of Malang City as well as settlements and activities of the surrounding community. At this three-way unsignalized intersection, the traffic volume is large, so it can cause congestion at certain times. The purpose of this study was to determine the performance of Three-way unsignalized intersection Muharto st. – Mayjen Sungkono St. – Raya Ki Ageng Gribig St at 2021. The research method based on Indonesian Highway Capacity Manual (MKJI 1997) published by th Directoral General of Highways. The result of this of the study found that the volume of vehicles at Muharto St. 1430 (vehicle/hour) – Mayjen Sungkono St. 1740 (vehicle/hour) – Raya Ki Ageng Gribig St. 1913 (vehicle/hour). So Qtotal is 3554 (smp/hour) and capacity of 2522 (smp/hour). So it is known that the value of the degree of saturation after the installation of alternative traffic lights that the degree of saturation is Degree of Saturation Muharto St. (DS) 0,38 with queue length (QL) 74 m, degree of saturation of Mayjen Sungkono St. (DS) 0,28 with queue length (QL) 86 m, degree of saturation of Raya Ki Ageng Gribig (DS) 0,52 with queue length (QL) 187 m.

Integrating public transit signal priority into max-pressure signal control: Methodology and simulation study on a downtown network

Max-pressure signal control has been analytically proven to maximize the network throughput and stabilize queue lengths whenever possible. Since there are many transit lines operating in the metropolis, the max-pressure signal control should be extended to multi-modal transportation systems to achieve more widespread usage. The standard max-pressure controller is more likely to actuate phases during high-demand approaches, which may end up ignoring the arrival of buses, especially in bus rapid transit. In this paper, we propose a novel max-pressure signal control that considers transit signal priority of bus rapid transit systems to achieve both maximum stability for private vehicles and reliable transit service. This study revises the original max-pressure control to include constraints that provide priority for buses. Furthermore, this policy is decentralized which means it only relies on it relies only on the local conditions of each intersection. We set the simulation on the real-world road network with bus rapid transit systems. Numerical results show that the max-pressure signal control which considers transit signal priority can still achieve maximum stability compared with other signal control integrated with transit signal priority. Furthermore, the max-pressure control reduces private vehicle travel time and bus travel time compared to the current signal control.

Adaptive forecast-driven repositioning for dynamic ride-sharing

AbstractIn dynamic ride-sharing systems, intelligent repositioning of idle vehicles often improves the overall performance with respect to vehicle utilization, request rejection rates, and customer waiting times. In this work, we present a forecast-driven idle vehicle repositioning algorithm. Our approach takes a demand forecast as well as the current vehicle fleet configuration as inputs and determines suitable repositioning assignments for idle vehicles. The core part of our approach is a mixed-integer programming model that aims to maximize the acceptance rate of anticipated future trip requests while minimizing vehicle travel times for repositioning movements. To account for changes in current trip demand and vehicle supply, our algorithm adapts relevant parameters over time. We embed the repositioning algorithm into a planning service for vehicle dispatching. We evaluate our forecast-driven repositioning approach through extensive simulation studies on real-world datasets from Hamburg, New York City, Manhattan, and Chengdu. The algorithm is tested assuming a perfect demand forecast and applying a naïve forecasting model. These serve as an upper and lower bound on state-of-the-art forecasting methods. As a benchmark algorithm, we utilize a reactive repositioning scheme. Compared to this, our forecast-driven approach reduces trip request rejection rates by an average of 3.5 percentage points and improves customer waiting and ride times.

Autonomous Traffic System for Emergency Vehicles

An emergency can occur at any time. To overcome that emergency efficiently, we require seamless movement on the road to approach the destination within a limited time by using an Emergency Vehicle (EV). This paper proposes an emergency vehicle management solution (EVMS) to determine an efficient vehicle-passing sequence that allows the EV to cross a junction without any delay. The proposed system passes the EV and minimally affects the travel times of other vehicles on the junction. In the presence of an EV in the communication range, the proposed system prioritizes the EV by creating space for it in the lane adjacent to the shoulder lane. The shoulder lane is a lane that cyclists and motorcyclists will use in normal situations. However, when an EV enters the communication range, traffic from the adjacent lane will move to the shoulder lane. As the number of vehicles on the road increases rapidly, crossing the EV in the shortest possible time is crucial. The EVMS and algorithms are presented in this study to find the optimal vehicle sequence that gives EVs the highest priority. The proposed solution uses cutting-edge technologies (IoT Sensors, GPS, 5G, and Cloud computing) to collect and pass EVs’ information to the Roadside Units (RSU). The proposed solution was evaluated through mathematical modeling. The results show that the EVMS can reduce the travel times of EVs significantly without causing any performance degradation of normal vehicles.

A Sustainable Model for Emergency Medical Services in Developing Countries: A Novel Approach Using Partial Outsourcing and Machine Learning.

IntroductionUnlike Western countries, many low- and middle-income countries (LMIC), like India, have a de-centralized emergency medical services (EMS) involving both semi-government and non-government organizations. It is alarming that due to the absence of a common ecosystem, the utilization of resources is inefficient, which leads to shortage of available vehicles and larger response time. Fragmentation of emergency supply chain resources motivates us to propose a new vehicle routing and scheduling model equipped with novel features to ensure minimal response time using existing resources.Materials and MethodsThe data set of medical and fire-related emergencies from January 2018 to May 2018 of Uttarakhand State in India was provided by GVK Emergency Management and Research Institute (GVK EMRI) also known as 108 EMSs was used in the study. The proposed model integrates all the available EMS vehicles including partial outsourcing to non-ambulatory vehicles like police vans, taxis, etc., using a novel two-echelon heuristic approach. In the first stage, an offline learning model is developed to yield the deployment strategy for EMS vehicles. Seven well researched machine learning (ML) algorithms were analyzed for parameter prediction namely random forest (RF), convolutional neural network (CNN), k-nearest neighbor (KNN), classification and regression tree (CART), support vector machine (SVM), logistic regression (LR), and linear discriminant analysis (LDA). In the second stage, a real-time routing model is proposed for EMS vehicle routing at the time of emergency, considering partial outsourcing.Results and DiscussionThe results indicate that the RF classifier outperforms the LR, LDA, SVM, CNN, CART and NB classifier in terms of both accuracy as well as F-1 score. The proposed vehicle routing and scheduling model for automated decision-making shows an improvement of 42.1%, 54%, 27.9% and 62% in vehicle assignment time, vehicle travel time from base to scene, travel time from scene to hospital, and total response time, respectively, in urban areas.

IPDALight: Intensity- and phase duration-aware traffic signal control based on Reinforcement Learning

Reinforcement Learning (RL) has been recognized as one of the most effective methods to optimize traffic signal control. However, due to the inappropriate design of RL elements (i.e., reward and state) for complex traffic dynamics, existing RL-based approaches suffer from slow convergence to optimal traffic signal plans. Meanwhile, to simplify the traffic modeling, most optimization methods assume that the phase duration of traffic signals is constant, which strongly limits the RL capability to search for traffic signal control policies with shorter average vehicle travel time and better GreenWave control. To address these issues, this paper proposes a novel intensity- and phase duration-aware RL-based method named IPDALight for the optimization of traffic signal control. Inspired by the Max Pressure (MP)-based traffic control strategy used in the transportation field, we introduce a new concept named intensity, which ensures that our reward design and state representation can accurately reflect the status of vehicles. By taking the coordination of neighboring intersections into account, our approach enables the fine-tuning of phase duration of traffic signals to adapt to dynamic traffic situations. Comprehensive experimental results on both synthetic and real-world traffic scenarios show that, compared with the state-of-the-art RL methods, IPDALight can not only achieve better average vehicle travel time and greenwave control for various multi-intersection scenarios, but also converge to optimal solutions much faster.

GAN and Multi-Agent DRL Based Decentralized Traffic Light Signal Control

Adaptive traffic light signal control (ATSC) is a promising paradigm for alleviating traffic congestion in intelligent transportation systems. Most of the existing methods require heavy traffic data exchange among neighboring intersections to achieve collaborative ATSC, which may not be supported by bandwidth-limited communication links in practice. In this article, we develop a communication-efficient decentralized ATSC framework for traffic networks with multiple intersections, where each intersection only exchanges traffic statistics with its neighboring intersections. In particular, the proposed framework consists of a generative adversarial network (GAN) based algorithm for traffic data recovery, and a multi-agent deep reinforcement learning (DRL) based decentralized ATSC algorithm for traffic efficiency enhancement. By adopting the value decomposition technique that establishes a nonlinear mapping from the local state-action values to the global reward, each intersection can independently determine its traffic light signal based on its local traffic data while achieving collaboration among neighboring intersections. Our proposed decentralized ATSC framework is scalable to large-scale traffic networks, and is also robust to traffic flow variations via interacting with the environment. Simulations show that our proposed algorithm can significantly reduce the vehicle travel time while maintaining high and stable traffic throughput.

An Adaptive Route Planning Method of Connected Vehicles for Improving the Transport Efficiency

In recent years, the route-planning problem has gained increased interest due to the development of intelligent transportation systems (ITSs) and increasing traffic congestion especially in urban areas. An independent route-planning strategy for each in-vehicle terminal improves its individual travel efficiency. However, individual optimal routes pursue the maximization of individual benefit and may contradict the global benefit, thereby reducing the overall transport efficiency of the road network. To improve traffic efficiency while considering the travel time of individual vehicles, we propose a new dynamic route-planning method by innovatively introducing a bidding mechanism in the connected vehicle scenario for the first time. First, a novel bidding-based dynamic route planning is proposed to formulate vehicle routing schemes for vehicles affected by congestion via the bidding process. Correspondingly, a bidding price incorporating individual and global travel times was designed to balance the travel benefits of both objectives. Then, in the bidding process, a new local search algorithm was designed to select the winning routing scheme set with the minimum bidding price. Finally, the proposed method was tested and validated through case studies of simulated and actual driving scenarios to demonstrate that the bidding mechanism would be conducive to improving the transport efficiency of road networks in large-scale traffic flow scenarios. This study positively contributes to the research and development of traffic management in ITSs.

GIS and Gravity Model-Based Accessibility Measure for Delhi Metro

Metro is a rapid transit mode that helps in fast and efficient movement of the public. To make metro journey more convenient and attractive, from origin to destination, accessibility of metro must be good. In this study, tri-level analysis is carried out for the study area of Delhi. Traffic congestion and environmental condition of Delhi are degrading continuously, and to improve the situation, a sustainable Multi Modal Transport System (MMTS) with high accessibility must be explored. In this paper, policy to improve zone to zone metro accessibility for work purpose is discussed and the relation between multimodal transport system with accessibility is studied using GIS (ArcGIS), macrosimulation software (VISUM) and gravity model. It was observed that by the addition and extension of metro lines, transport accessibility is greatly improved. This study shows that percentage of zones in Delhi with higher accessibility level could increase from 5 to 13.88% with the implementation of phase IV of metro. In addition, the implementation of phase IV metro could reduce the average travel time between zones and metro stations. It will also reduce the travel time between metro stations substantially (for the zones covered in this study the average travel time is reduced by 23.63% and for the station covered in this study the average travel time is reduced by 17.73%). Further, it was found that ‘out vehicle travel time’ (OVTT) is having a major impact on the accessibility of metro. Lastly, it was concluded that with the higher level of metro accessibility, people will prefer the multimodal transport system with metro as the main mode. Travel time isochrones for public and private trips for the estimated MMTS model are also presented in this paper to depict the changes in accessibility for different scenarios. This will further help in improving private transport accessibility by reducing congestion on roads. Thus, planning for transport accessibility helps in developing the city in a sustainable manner in the future.

Revealing the spatial variation in vehicle travel time with weather and driver travel frequency impacts: Findings from the Guangdong–Hong Kong–Macao Greater Bay Area, China

<abstract> <p>Vehicle travel time information is an essential location-based services that can be used to assess highway traffic conditions and provide valuable insights for transit agencies and travelers. To reveal the spatial variation in vehicle travel time with multiple factors, a multiple regression model and a geographically weighted regression model are used to investigate the associations between travel time and various factors. This study draws on freeway toll data in combination with local weather station records on Fridays over 12 months (286, 406 travel information data points), and the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), China, is used as a case study for examining the influence of weather and driver travel frequency on vehicle travel time. The results show that i) travel frequency along an origin-destination (OD) route has a significant effect on travel time, and this effect is approximately 3 to 100 times that of other explanatory variables; ii) rainfall significantly impacts travel time, with an effect that is 1.9 to 8.26 times that of other weather factors; and iii) both weather and driver travel frequency factors display spatial heterogeneity. These findings provide valuable insights for both traffic management and freeway travelers.</p> </abstract>

Research on simulation and optimization of road traffic flow based on Anylogic

With the development of urban roads and the growth of traffic flow, large-scale congestion often occurs. The long time-consuming urban road traffic makes drivers very annoying. This is the main reason for using simulation tools to analyze and optimize urban road traffic flow. Intelligent body modeling and simulation has been regarded as a reliable and powerful tool in the field of traffic simulation. This paper uses Anylogic software based on intelligent modeling and simulation to solve the traffic flow optimization problem in a congested area in Tianjin. The simulation results show that by changing the display time of the traffic lights in the road network, the vehicle travel time in the system is reduced from 145.46 seconds to 132.31 seconds. After 300 iterations of experiments, it was determined that the vehicle’s travel time in the system reached the optimum when iterated 73 times.

Algorithm for Creating Optimized Green Corridor for Emergency Vehicles with Minimum Possible Disturbance in Traffic

Abstract Green corridor is a dynamic real-time lane created in order to increase the speed at which an emergency vehicle can travel in traffic. Its purpose is to reduce the travel time of emergency vehicles. The paper submitted examines ways to optimize the travel time for emergency vehicles and for other drivers on the roads. The paper takes into account the fact that all vehicles accelerate and decelerate at different speeds and the fact that there might be several traffic guided lights and non-guided lights on the roads. There are other factors considered, such as the speed and safety, in order to create a sustainable solution that can be implemented at scale. SUMO simulation libraries are used to create an environment as close to reality as possible. The trade-off between the number of variables selected and the approximation of a real situation has been carefully selected so that the solution is also feasible in real-life situations.

Multiagent Meta-level Control for Adaptive Traffic Systems: A Case Study

As cities across the globe continue to grow, traffic congestion has become globally ubiquitous with great economic and environmental costs associated with it. The increasing prevalence of self-driving vehicles creates an opportunity to build smart, responsive traffic infrastructure of the future. Such an infrastructure consisting of connected and autonomous vehicles and smart traffic lights would have the potential to cope with congestion, weather phenomena and accidents, while maintaining safety and ensuring privacy of information. This paper introduces an approach to address the challenge of dynamically adjusting traffic to the changes in the environment. We argue that multiagent meta-level control (MMLC) is an effective way to non-myopically determine how and when this adaptation should be done. The approach highlights the role of dynamic meta-reasoning in a platooning scenario, in which collaboration contributes to improved travel time for vehicles in the network as well as a positive environmental impact as related to fuel consumption and emissions. Specifically, for the case study described in the paper, our MMLC-based approach leads to approximately 44% decrease in travel time, 7% increase in average speed, a 32% decrease in fuel consumption and a 35% drop in emissions. We also see performance advantages for a scaled-up mixed traffic simulation environment.

Research on travel time prediction of expressway in peak period based on Greenberg model

Expressway travel time is an important parameter to describe the traffic status, which can accurately reflect the efficiency of expressway traffic. To further simplify the complexity of the travel time prediction method, and improve the prediction accuracy, in this paper, the travel time prediction of the expressway is divided into three cases for discussion. First of all, based on the Greenberg model, under the premise of a comprehensive analysis of the section flow, traffic density, and other factors, to establish different sections under the peak period expressway vehicle travel time prediction model. Finally, the model is verified by taking the expressway around the city as an example. The results shows that the prediction results are always within 10% of the actual measurement error, which shows that compared with the measured data, the error of the model proposed is small, the prediction accuracy is high, within the acceptable range.