Vehicle Travel Time Research Articles

Enhancing Urban Intersection Efficiency: Visible Light Communication and Learning-Based Control for Traffic Signal Optimization and Vehicle Management

This paper introduces a novel approach, Visible Light Communication (VLC), to optimize urban intersections by integrating VLC localization services with learning-based traffic signal control. The system enhances communication between connected vehicles and infrastructure using headlights, streetlights, and traffic signals to transmit information. Through Vehicle-to-Vehicle (V2V) and Infrastructure-to-Vehicle (I2V) interactions, joint data transmission and collection occur via mobile optical receivers. The goal is to reduce waiting times for pedestrians and vehicles, enhancing overall traffic safety by employing flexible and adaptive measures accommodating diverse traffic movements. VLC cooperative mechanisms, transmission range, relative pose concepts, and queue/request/response interactions help balance traffic flow and improve road network performance. Evaluation in the SUMO urban mobility simulator demonstrates advantages, reducing waiting and travel times for both vehicles and pedestrians. The system employs a reinforcement learning scheme for effective traffic signal scheduling, utilizing VLC-ready vehicles to communicate positions, destinations, and routes. Agents at intersections calculate optimal strategies, communicating to optimize overall traffic flow. The proposed decentralized and scalable approach, especially suitable for multi-intersection scenarios, showcases the feasibility of applying reinforcement learning in real-world traffic scenarios.

Optimizing passenger vehicle travel time with Model Predictive Control in multi-region traffic networks

Abstract This study investigates the impact of truck traffic on passenger vehicles in an urban network. Utilizing the Macroscopic Fundamental Diagram (MFD), a methodology to calculate the travel time spent (TTS) by passenger vehicles has been developed. To address this issue, an optimal control problem was formulated and solved using a Model Predictive Control (MPC) approach. The MPC framework has been applied in a centralized manner, to manage accumulation for various modes. To explore different traffic management strategies, the centralized MPC technique was implemented in two distinct configurations: region-based and vehicle-based approaches. It has been tested for various vehicle mixes and multiple control scenarios to assess the effectiveness in reducing passenger travel time spent (PTTS) and vehicle accumulation. The results demonstrate that the vehicle-based MPC approach tends to minimize the number of vehicles more effectively compared to the region-based approach. However, in terms of reducing passenger travel time, the region-based approach outperforms the vehicle-based strategy. This is attributed to enhanced coordination among traffic flow controllers, highlighting the importance of strategic controller interactions in urban traffic management systems. This research enhances both the theoretical framework for optimizing traffic flow and provides valuable practical insights for city planners and engineers aiming to deploy advanced traffic management strategies. Future studies could explore the scalability of these control systems and their capability to integrate real-time traffic data.

Simulasi 3 Skenario Perbaikan Kemacetan Jagir Wonokromo Dampak dan Implementasi

Traffic congestion in Indonesian cities is a major problem, due to uneven mobility. The Jagir Wonokromo intersection, one of the busiest intersections in Surabaya, experiences traffic congestion during rush hour. This congestion can have a variety of negative impacts, such as air pollution and queuesat each intersection. This study analyzes the traffic congestion problems at the Jagir Wonokromo intersection and proposes the appropriate improvement scenario. A simulation design was developed to test three traffic congestion improvement scenarios at the Jagir Wonokromo intersection in Surabaya. The study sample was 40 vehicles that passed through during rush hour. The simulation results showed that scenario 3 is the most appropriate improvement scenario. This scenario successfully reduced the time in the system by 20.2% compared to the initial condition. The impact of scenario 3 is that traffic congestion at the intersection is reduced, but the travel time of vehicles from the DTC direction to Jalan Jagir Wonokromo also increases. Scenario 3 is considered to be in accordance with the conditions of the intersection and can be easily implemented. The study of traffic congestion at the Jagir Wonokromo intersection offers opportunities for the government and the public to address traffic congestion with effective simulation of improvement scenarios.

Mapping Access to Children's Hospitals in Texas.

Vehicle access, travel time, and distance to hospitals and emergency rooms with sufficient patient beds are critical healthcare accessibility measures, especially for children who require specific pediatric services. In a large state like Texas with vast rural areas and limited public transit infrastructure, 75% of the children live over an hour from the closest facility that provides pediatric emergency services or specialty care. In view of this challenge, this study first sought to map the prevailing geographical accessibility to children's hospitals and, second, to model the hospital beds per capita for each hospital's service area within the state of Texas. The results showed disparities in accessing emergency pediatric care, especially in rural areas. However, despite major metro areas recording better geographical accessibility to pediatric healthcare, residents in these areas may experience limited hospital bed availability. The findings indicate an urgent need for more pediatric healthcare services in rural Texas. Given the increasing population growth in metro areas and their surroundings, there is also a need for the expansion of healthcare infrastructure in these areas.

Analisis Pengaruh Kendaraan Yang Melakukan Putar Balik Terhadap Kinerja Ruas Jalan (Studi Kasus Pada U–Turn Jl. Bypass Padang)

Transportation has an important role in human life, because transportation has a big influence on society, economic and social political development of a country. research discusses analysis of influence of vehicles making a U-turn on Bypass No. KM 15, Aie Pacah, Tangah City, Padang City, West Sumatra. Research purposes is to determine level of service that occurs in traffic flow, to obtain this aim PKJI 2014 is used. This research obtains average travel time for vehicles making a U-Turn, length of queue when making a U-Turn, average travel time average number of vehicles disturbed and not disturbed by U-Turns and knowing level of service on that road section. From data processing it can be concluded that peak hour vehicle speeds are slower than non-peak hour vehicle speeds due to high traffic flow. Meanwhile, waiting time when a vehicle is about to turn around, during peak hours vehicles wait longer than during non-peak hours because volume of vehicles is denser during peak hours. Average queue length in opposite direction during peak hours is longer than during non-peak hours due to the density of vehicles during peak hours, it can be concluded that level of service C.

Effect of Heavy Vehicles on Road Pavement and Implication on the Environment in North Central Nigeria

Transport infrastructure plays a crucial role in Nigeria’s economic development, mobility and environmental sustainability. Road pavement facilitates the movement of vehicles and ensure safe and efficient transportation of goods, services and people across the country. The study was conducted on Asa-dam Road Ilorin, Kwara State, Nigeria, primary source of data and random sampling techniques was employed to select 150 respondents. Findings from the study revealed that potholes, depression of the road surface and other road failures are caused by heavy vehicles. Majority of respondents (96.7%) perceived highest level of major damage are caused by heavy vehicles of 50 tons above while 45.3% perceived minor damage and 34.7% perceived medium damage by heavy vehicles of less than 30 tons. P-value of 0.000 is below the conventional threshold of p<0.05. R value of 0.892 with R2 value of 0.795 implied a strong and positive relationship exist between inadequate road pavement and the environment which increases vehicle maintenance cost, travel time and pollution. F-ratio value of 140.832 was statistically significant. The study concluded that heavy vehicles have significant effect on road pavement damage as a result of the weight and of goods they carry therefore affecting the environment. However, it is recommended that the current road pavement be scrapped and new layer of asphalt be laid to cater for heavy vehicle traffic while government should do more routine and periodic maintenance of the road pavement.

DERLight: A Deep Reinforcement Learning Traffic Light Control Algorithm with Dual Experience Replay

<p>In recent years, with the increasingly severe traffic environment, most cities are facing various traffic congestion problems, and the demand for intelligent regulation of traffic signals is also increasing. In this study, we propose a new intelligent traffic light control algorithm, dual experience replay light (DERLight), which innovatively and efficiently designs a dual experience replay training mechanism based on the classic deep Q network (DQN) framework and considers the dynamic epoch function. As results show that compared with some state-of-the-art algorithms, DERLight can shorten the average travel time of vehicles, increase the throughput at intersections, and also speed up the convergence of the network. In addition, the design of this algorithm framework is not only limited to the field of intelligent transportation, but also has transferability for some other fields.</p> <p> </p>

Max-Pressure Control utilizing Total Movement Delay

Traffic signal control algorithms utilizing max-pressure principles can rely on feedback obtained from either queue lengths or travel times. This paper considers max-pressure algorithms based on travel times, aiming to address traffic-related challenges, including (i) unbounded link capacity, (ii) mitigating the “the Last Packet effect”, and (iii) enhancing queue modeling to account for spatial queue distribution. Hence, we propose a new max-pressure algorithm based on total movement delays, which requires local information only to determine appropriate phase activation, and aims at minimizing delays. First, we introduce a new traffic model based in queuing theory. Unlike previous approaches that rely on the store-and-forward model, our model accurately depicts vehicle travel time delays. Second, based on the developed model, we introduce a new traffic oriented max-pressure algorithm. The objective of maximizing throughput is compared to minimizing travel time delays, through the comparison of different a-cyclic time-based max-pressure control schemes with existing a-cyclic queue-based algorithms control schemes.

Robust Emergency Preparedness Planning for Resilience Enhancement of Energy-Transportation Nexus Against Extreme Rainfalls

This paper proposes a robust emergency preparedness planning (EPP) scheme to optimally pre-position diverse mobile emergency resources (MERs) in staging locations and perform proactive network reconfiguration for resilience enhancement of energy-transportation nexus (ETN) against extreme rainfalls. Based on rainfall-runoff simulation with hydrodynamic partial differential equations, a risk identification approach for flood-prone transportation modeling is developed for vehicle travel time estimation and MER routing optimization. To handle uncertainties in line outages and flooded roads incurred by extreme rainfalls, a tri-level robust EPP model is proposed to determine the optimal emergency preparedness plan immunized against the worst-case realization of uncertainties. Furthermore, a tailored solution method combining nested column-and-constraint generation algorithm with multiple linearization techniques is devised to cope with the proposed nonlinear robust EPP model. Comparative studies have validated the effectiveness of the proposed scheme for resilience enhancement of the ETN confronted with extreme rainfall disasters.

Shortening Emergency Medical Response Time with Joint Operations of Uncrewed Aerial Vehicles with Ambulances

Problem definition: Uncrewed aerial vehicles (UAVs) are transforming emergency service logistics applications across sectors, offering easy deployment and rapid response. In the context of emergency medical services (EMS), UAVs have the potential to augment ambulances by leveraging bystander assistance, thereby reducing response times for delivering urgent medical interventions and improving EMS outcomes. Notably, the use of UAVs for opioid overdose cases is particularly promising as it addresses the challenges faced by ambulances in delivering timely medication. This study aims to optimize the integration of UAVs and bystanders into EMS in order to minimize average response times for overdose interventions. Methodology/results: We formulate the joint operation of UAVs with ambulances through a Markov decision process that captures random emergency vehicle travel times and bystander availability. We apply an approximate dynamic programming approach to mitigate the solution challenges from high-dimensional state variables and complex decisions through a neural network-based approximation of the value functions (NN-API). To design the approximation, we construct a set of basis functions based on queueing and geographic properties of the UAV-augmented EMS system. Managerial implications: The simulation results suggest that our NN-API policy tends to outperform several noteworthy rule- and optimization-based benchmark policies in terms of accumulated rewards, particularly for situations that are primarily characterized by high request arrival rates and a limited number of available ambulances and UAVs. The results also demonstrate the benefits of incorporating UAVs into the EMS system and the effectiveness of an intelligent real-time operations strategy in addressing capacity shortages, which are often a problem in rural areas of the United States. Additionally, the results provide insights into specific contributions of each dispatching or redeployment strategy to overall performance improvement. Funding: This work was supported by the National Science [Grant 1761022]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0166

A spatiotemporal control method at isolated intersections under mixed‐autonomy traffic conditions

AbstractWith the introduction of connected and automated vehicles (CAVs), the integrated control of traffic signals, lane assignments, and vehicle trajectories becomes feasible, offering notable benefits for enhancing intersection operations. However, during the prolonged transition to an entirely CAV environment, how to fully leverage the advantage of CAVs while considering the characteristics of human‐driven vehicles remains a huge challenge. To address this challenge, this paper proposes a joint optimization method for spatiotemporal resources at isolated intersections under mixed‐autonomy traffic conditions. Initially, the lane assignment optimization problem is modeled as a mixed integer linear program model to maximize the reserve capacity. Subsequently, the signal‐vehicle coupled control is formulated as a dynamic programming model with the objective of reducing vehicle travel time. Additionally, criteria are established to assess the need for re‐optimizing lane assignments. Simulations validate the superiority of the proposed control method over adaptive control in terms of traffic efficiency and intersection capacity amid substantial traffic demand fluctuations. Sensitivity analyses reveal that the proposed control method can yield higher benefits under medium traffic demand levels. Furthermore, the proposed algorithm exhibits no significant sensitivity to the CAV market adoption rate, suggesting its applicability throughout the CAV adoption process.

Algorithm for a simulation model for the selection of a rational type of vans on technological routes of the transport and recycling system for recycling of a metallurgical enterprise

The article presents an algorithm of the simulation model for selecting the rational type of trucks on the technological routes of the transport and production system of waste transportation in the conditions of a metallurgical enterprise. The study was conducted on the basis of formalized models and queuing systems theory. In the simulation, a discrete-event model of a closed queuing system was chosen as the basic model. In this model, cars are represented as requests that go through the following phases of service: loading at the temporary waste storage dump; movement to the unloading point (crushing and sorting complex); unloading raw materials to the crushing and sorting complex; returning to the loading point along the same route; servicing of vehicle breakdowns. The following data were used to build the simulation model through statistical studies of the transportation process on technological routes: vehicle loading time, vehicle unloading time, and time determined by the duration of vehicle failure. When constructing the algorithm of the simulation model, the duration of one experiment and the number of simulation experiments were determined using the methods of mathematical statistics and the theory of experiment planning. The modeling process begins with the input of the following initial data: the number of vehicle units, the average vehicle travel time, the current amount of raw materials in the crushing and sorting complex, the vehicle carrying capacity, the intensity of raw material supply to the crushing and sorting complex, the vehicle unloading time, the intensity of minor vehicle breakdowns, and the intensity of vehicle repair. The algorithm is built in accordance with the principles of special states and meets the requirements for discrete-event models. As a result of the algorithm development, it will be possible to determine the required number (types) of vehicles necessary to ensure the smooth functioning of this transport and production system, to determine the idle time coefficient and the coefficient of time losses due to technological delays of vehicles.
 Key words: modeling, simulation model, dump truck, carrying capacity, metallurgical slag, crushing and screening equipment.

Optimizing Freight Vehicle Routing in Dynamic Time-Varying Networks with Carbon Dioxide Emission Trajectory Analysis

In this study, we formulate a freight vehicle path-planning model in the context of dynamic time-varying networks that aims to capture the spatial and temporal distribution characteristics inherent in the carbon dioxide emission trajectories of freight vehicles. Central to this model is the minimization of total carbon dioxide emissions from vehicle distribution, based on the comprehensive modal emission model (CMEM). Our model also employs the freight vehicle travel time discretization technique and the dynamic time-varying multi-path selection strategy. We then design an improved genetic algorithm to solve this complicated problem. Empirical results vividly illustrate the superior performance of our model over alternative objective function models. In addition, our observations highlight the central role of accurate period partitioning in time segmentation considerations. Finally, the experimental results underline that our multi-path model is able to detect the imprint of holiday-related effects on the spatial and temporal distribution of carbon dioxide emission trajectories, especially when compared to traditional single-path models.

Exploring bus tracking data to characterize urban traffic congestion

Quantification of traffic dynamics is a valuable tool for city planning and management. Metrics such as the vehicle average speed, travel time, delays, and count of stops, can be used to characterize mobility and traffic congestion in an area. However, effective study of mobility data is often hindered by the difficulty of gathering mobility data in a practical, inexpensive, and prompt way.In this work, we explore the use of city buses as mobility probes, using the existing smart city infrastructure deployed in Aveiro, Portugal. We propose a method for traffic congestion detection considering the low vehicle speed, low traffic flow and road occupancy close to its capacity. Three degrees of congestion are identified using the k-means approach; DBSCAN is used to characterize the typical level of congestion in a road. Using four-weeks of mobility data, it was possible to assess the congestion along the day and for the different days of the week; some road segments proved to be consistently prone to congestion. We also studied parameters of driving safety, considering speed and acceleration.In this work, we show that knowledge discovery can be applied to mobility data being collected by tracking buses, exploring data that is often collected for other purposes also to characterize traffic congestion. These methods can inform decision makers and are easily ported to other cities.

Investigating the min‐cost minimum fleet problem through taxi data analysis

AbstractThe optimization of urban traffic efficiency and reduction of pollution through minimizing the number of taxis has become a topic of increasing interest. However, the problem of determining the minimum fleet that considers both time and distance efficiency has received limited attention. Furthermore, little research has been done on how this problem is influenced by factors such as city size and travel demand (i.e., the number of trips). This study extends the minimum fleet problem to the min‐cost minimum fleet problem, and proposes a method to address the problem by constructing a “dispatch network” and finding the minimum weight and minimum path cover of it. A comparative analysis of the method is conducted with the taxi trajectory data of three cities, New York, Chengdu, and Chicago, and the results show a reduction in the off‐load travel time of vehicles compared to the previous method, while maintaining the same fleet size. With the introduction of an emissions model and its combination with our methodology, emissions can also be minimized. Moreover, the relationship between min‐cost minimum fleet and city size and travel demand (number of trips) is studied. Experimental results indicate that fleet size and idle time (distance) vary linearly with the number of trips. Meanwhile, city size also has an essential impact on the algorithm's performance. Furthermore, these results indicate that the min‐cost minimum fleet will help small cities to achieve higher efficiency (smaller fleet and less connection time) than large cities. The acceleration of urbanization is driving changes in the transportation system. Our findings are expected to have significant implications for transport planning, resource conservation, and reducing pollution.

Traffic Signal Control With Adaptive Online-Learning Scheme Using Multiple-Model Neural Networks.

This article proposes a new traffic signal control algorithm to deal with unknown-traffic-system uncertainties and reduce delays in vehicle travel time. Unknown-traffic-system dynamics are approximated using a recurrent neural network (NN). To accurately identify the traffic system model, an online-learning scheme is developed to switch among a set of candidate NNs (i.e., multiple-model NNs) based on their estimation errors. Then, a bank of optimal signal-timing controllers is designed based on the online identification of the traffic system. Simulation studies have been carried out for the obtained control strategies using multiple-model NNs, and the desired results have been obtained. Moreover, compared with the widely used actuated traffic signal control schemes, it is shown that the proposed method can reduce vehicle travel delays and improve traffic system robustness.

Coupling Control of Traffic Signal and Entry Lane at Isolated Intersections Under the Mixed-Autonomy Traffic Environment

There is a growing number of studies on the traffic control strategies of signal timings and vehicle trajectories at signalized intersections, while lane assignments are widely pre-specified and fixed. Meanwhile, existing strategies generally require a fully connected and automated vehicles (CAVs) environment. To fill up the gaps, this study contributes to a two-dimensional (spatiotemporal) control strategy by jointly optimizing traffic signals, lane settings, and vehicle trajectories at isolated signalized intersections under the mixed traffic of connected automated and human-driven vehicles. Specifically, based on the pseudo-platoons, signal timing plans and settings of approach lanes are jointly optimized by a piece-wise linear programming model. Then, vehicle trajectory control is integrated into the collaborative control framework to smooth vehicle trajectories. Three groups of numerical experiments are conducted to verify the effectiveness and efficiency of the proposed control method. Results show that the proposed algorithm outperforms the actuated control in terms of vehicle travel time under both under-saturated and over-saturated traffic conditions.

Batch-based vehicle tracking in smart cities: A Data fusion and information integration approach

A data fusion and information integration (DFII-VT) framework is proposed to solve the batch-based vehicle matching/tracking problem using heterogeneous fixed-location and mobile sensing data available in smart cities. To make the model more realistic, traffic knowledge such as lane-choice decision, traffic merging, travel time and vehicle characteristics are calibrated using the historical dataset and then integrated into the model. The problem can be formulated as a combinatorial optimization model, and solved using a dynamic programming/Kuhn-Munkres algorithm-based two-step approach. By doing so, the proposed method can obtain individual travel times of the matched data pairs, and these can be directly used to estimate the corridor travel times of individual vehicles. The experimental results show that the fusion of mobile sensing data and fixed-location data yields significantly better results than using single-source data. Significant improvements in matching accuracy are obtained as more traffic information is integrated into the model. Therefore, the method may be considered a framework for integrating the manifold traffic information acquired within smart cities to obtain more accurate matching results and further used to optimize other fine-grained traffic applications, such as estimating vehicle trajectories along arterial corridors, estimating individual vehicle-based fuel consumption/emissions, and helping to infer real-time queuing processes at signalized intersections. The paper also studies some practical issues related to the use of heterogeneous traffic data, such as data errors, and detection failures.

Real-time estimation of multi-class path travel times using multi-source traffic data

In practice, most of the intelligent transportation systems provide average travel times of all vehicles on selected paths in real time on a regular basis. However, path travel times of different vehicles could vary widely under different traffic conditions. There is a need to consider the differences in vehicle classes for path travel time estimation. This paper proposes a novel modeling framework that considers variance–covariance relationships between vehicle classes for real-time estimation of multi-class path travel times with use of multi-source traffic data collected from various types of sensors. The proposed methodology is examined with a case study of a selected urban expressway in Hong Kong with data obtained from multiple sources. The path travel time estimates by vehicle class are validated and the results demonstrate the merits and performance of the proposed framework.

AUBURN, CA, EVACUATION PLAN

Abstract. Auburn is a small but famous gold mining and railroad town in the Sierra Nevada Mountains of Placer County, California. The city has seen recent and historically reoccurring wildfires. In 2021, 1.5 million acres burned in the Sierra Nevada Mountains, a new record. Auburn consists of three (3) nonadjacent and nonoverlapping sections or parcels of land. The three (3) centroids of each section of Auburn are identified and used in three (3) evacuation scenarios: First, Auburn is evacuated away from the three (3) centroids. We look at how far evacuees can reach in 5, 10, 15, and 30 minutes using emergency vehicle travel times, so signal lights, one-way streets, etc., are ignored. Second, we evacuate away from the three (3) centroids while also avoiding previous burn areas. Third, we evacuate Auburn toward the only area hospital, a structure likely to be defended from fire by first responders. Also, some evacuees might require medical attention. Future work includes looking at how far emergency vehicles from places like fire stations can reach in 5, 10, 15, and 30 minutes, providing a coverage area for first responders.