Vehicle Travel Time Research Articles

Electric Vehicle Routing Problem with Battery Swapping Considering Energy Consumption and Carbon Emissions

In this paper, we study an electric vehicle routing problem while considering the constraints on battery life and battery swapping stations. We first introduce a comprehensive model consisting of speed, load and distance to measure the energy consumption and carbon emissions of electric vehicles. Second, we propose a mixed integer programming model to minimize the total costs related to electric vehicle energy consumption and travel time. To solve this model efficiently, we develop an adaptive genetic algorithm based on hill climbing optimization and neighborhood search. The crossover and mutation probabilities are designed to adaptively adjust with the change of population fitness. The hill climbing search is used to enhance the local search ability of the algorithm. In order to satisfy the constraints of battery life and battery swapping stations, the neighborhood search strategy is applied to obtain the final optimal feasible solution. Finally, we conduct numerical experiments to test the performance of the algorithm. Computational results illustrate that a routing arrangement that accounts for power consumption and travel time can reduce carbon emissions and total logistics delivery costs. Moreover, we demonstrate the effect of adaptive crossover and mutation probabilities on the optimal solution.

Dynamic Planning of Mobile Service Teams’ Mission Subject to Orders Uncertainty Constraints

This paper considers the dynamic vehicle routing problem where a fleet of vehicles deals with periodic deliveries of goods or services to spatially dispersed customers over a given time horizon. Individual customers may only be served by predefined (dedicated) suppliers. Each vehicle follows a pre-planned separate route linking points defined by the customer location and service periods when ordered deliveries are carried out. Customer order specifications and their services time windows as well as vehicle travel times are dynamically recognized over time. The objective is to maximize a number of newly introduced or modified requests, being submitted dynamically throughout the assumed time horizon, but not compromising already considered orders. Therefore, the main question is whether a newly reported delivery request or currently modified/corrected one can be accepted or not. The considered problem arises, for example, in systems in which garbage collection or DHL parcel deliveries as well as preventive maintenance requests are scheduled and implemented according to a cyclically repeating sequence. It is formulated as a constraint satisfaction problem implementing the ordered fuzzy number formalism enabling to handle the fuzzy nature of variables through an algebraic approach. Computational results show that the proposed solution outperforms commonly used computer simulation methods.

Assessment of the impact of the characteristics of the road network on the duration of the route

The “floating” car method using global positioning system (GPS) devices is used to collect data on the duration of traffic on city streets. However, a complete study of the transport network is time-consuming and includes additional financial investments. An alternative source of data collection is the use of rolling stock of public transport. Therefore, this article discusses the relationship between travel time by car and bus. It was found that the travel time of buses is usually more than the travel time of an individual vehicle. The impact of the characteristics of the road network, such as a number of regulated and unregulated intersections, a number of driveways, a number of bus stops, traffic intensity, a number of lanes and a number of turns on the duration of public transport, has been assessed. The results show the relationship between the considered durations of traffic and data from the board equipment of the vehicles of public transport can be used to estimate the duration of movement along the route taking into account the characteristics of the network.

Research on Lane-level Travel Time Prediction Method Based on Gated Recurrent Neural Network

In order to meet the needs of refined traffic guidance in the environment of the Internet of Vehicles, a lane-level travel time prediction method based on Gated Recurrent Neural Network is proposed. The research is based on the vehicle information collected by electronic police equipment, and analyzes and integrates the trip time of a single vehicle as model input; builds a gated recurrent unit(GRU) model deep learning framework, and performs multiple iterations through deviation correction and parameter update; The vehicle travel time data is verified and compared with the ARIMA model, BPNN model and LSTM model. The results show that the MAE and RMSE values of the GRU model are lower than other models, so it has good prediction accuracy.

Deep Reinforcement Learning Based Optimal Route and Charging Station Selection

This paper proposes an optimal route and charging station selection (RCS) algorithm based on model-free deep reinforcement learning (DRL) to overcome the uncertainty issues of the traffic conditions and dynamic arrival charging requests. The proposed DRL based RCS algorithm aims to minimize the total travel time of electric vehicles (EV) charging requests from origin to destination using the selection of the optimal route and charging station considering dynamically changing traffic conditions and unknown future requests. In this paper, we formulate this RCS problem as a Markov decision process model with unknown transition probability. A Deep Q network has been adopted with function approximation to find the optimal electric vehicle charging station (EVCS) selection policy. To obtain the feature states for each EVCS, we define the traffic preprocess module, charging preprocess module and feature extract module. The proposed DRL based RCS algorithm is compared with conventional strategies such as minimum distance, minimum travel time, and minimum waiting time. The performance is evaluated in terms of travel time, waiting time, charging time, driving time, and distance under the various distributions and number of EV charging requests.

Joint estimation of paths and travel times from Bluetooth observations

Bluetooth data sets allow a direct estimation of travel times across sensor pairs; however, the resulting estimations contain noise because of missed detection rate and alternative paths between sensors. Additionally, although Bluetooth data sets allow tracking of vehicles across sensors, they do not provide an exact path (i.e. a sequence of traffic sections). Estimating vehicle paths from Bluetooth records is not a straightforward task. This paper proposes a joint method to simultaneously infer vehicle paths and travel times using Bluetooth records. The methodology is applied in a case study of Bluetooth measurements in Brisbane, Australia. The results are validated against travel time observations, which are collated by creating a testing set out of the whole data set. Travel time estimates are robust to the changes in the size of the available measurements, and the proposed model significantly outperforms a naive model where travel times are estimated via direct matching.

ANALISIS ZONA SELAMAT SEKOLAH (ZoSS) DI KECAMATAN DENPASAR SELATAN (STUDI KASUS: SDN 5 PEDUNGAN DAN SEKOLAH HARAPAN)

SDN 5 Pedungan dan Sekolah Harapan merupakan sekolah di Kecamatan Denpasar Selatan yang sudah memiliki fasilitas Zona Selamat Sekolah (ZoSS). Kedua sekolah tersebut dilalui volume lalu lintas yang cukup tinggi karena banyaknya pusat bangkitan pergerakan yang berlokasi disepanjang jalan seperti pedagang, pendidikan dan industri. Penelitian ini bertujuan untuk menganalisis tingkat efektivitas ZoSS di kedua sekolah tersebut yang dilalui volume lalu lintas cukup tinggi dibandingkan dengan sekolah lainya. Survey lapangan dilakukan dengan mencatat waktu tempuh kendaraan saat melintasi ZoSS pada jarak 100 m. Tingkat efektivitas ZoSS dianalisa dengan menggunakan Peraturan Direktur Jendral Perhubungan Darat tahun 2006, 2014, dan 2018. Hasil analisis menunjukan bawha penerapan ZoSS di Kecamatan Denpasar Selatan tidak efektif, rata-rata prosentase kendaraan patuh saat melintasi ZoSS untuk SDN 5 Pedungan adalah 42.67% yang artinya ZoSS cukup efektif dan Sekolah Harapan adalah 32.34% yang artinya ZoSS kurang efektif.

Measurement of Mode-Wise Individual Delay Components and Prediction of Queue Length at a Signalized Intersection Under Heterogeneous Traffic Conditions

Accurate measurement of control delay and queue length is important for effective traffic management at signalized intersections and to assess its performance. The analytical methods which are commonly used to estimate delays are primarily developed for homogeneous lane-based traffic conditions and require lot of inputs for delay estimation. If cycle lengths are not the same for a signalized intersection operated by traffic police, then use of analytical methods is restricted. The present study tries to overcome these limitations by proposing an approach in which the individual delay components were directly measured using travel times from video as input and electric poles on median as reference points. Traffic captured through videographic survey at a busy intersection in Vellore, India, was used to illustrate the proposed approach. Instead of conventional stopped delay concept, a new delay called ‘Stop & Go’ delay which is more appropriate for heavily congested intersections has been proposed and used. This ‘Stop & Go’ delay concept captures the time lost due to movement of queue in addition to frequent stoppings which is rather not possible with stopped delay definition. It was found that the control delays experienced by different vehicle types are not the same and four-wheeler exhibits longer delays than two- and three-wheelers. One of the main advantages of the proposed approach is it does not require many parameters to calculate control delay and only travel times of sample vehicles are sufficient. Another advantage with the proposed approach is it can be applied to all types of signalized intersections irrespective of whether it has a fixed time signal or manually operated one. With the proposed approach, one can get vehicle class-wise individual delay components which may not be possible with the existing methods available in the literature. In addition to delay measurement, the present study also developed polynomial regression models to predict queue length at intersections using control delay as independent variable. The results are promising and can be used to predict the queue length at signalized intersections in India.

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.

Origin-Destination Flow Estimation from Link Count Data Only.

All established models in transportation engineering that estimate the numbers of trips between origins and destinations from vehicle counts use some form of a priori knowledge of the traffic. This paper, in contrast, presents a new origin–destination flow estimation model that uses only vehicle counts observed by traffic count sensors; it requires neither historical origin–destination trip data for the estimation nor any assumed distribution of flow. This approach utilises a method of statistical origin–destination flow estimation in computer networks, and transfers the principles to the domain of road traffic by applying transport-geographic constraints in order to keep traffic embedded in physical space. Being purely stochastic, our model overcomes the conceptual weaknesses of the existing models, and additionally estimates travel times of individual vehicles. The model has been implemented in a real-world road network in the city of Melbourne, Australia. The model was validated with simulated data and real-world observations from two different data sources. The validation results show that all the origin–destination flows were estimated with a good accuracy score using link count data only. Additionally, the estimated travel times by the model were close approximations to the observed travel times in the real world.

Endogenous stochastic optimisation for relief distribution assisted with unmanned aerial vehicles

Unmanned aerial vehicles (UAVs) have been increasingly viewed as useful tools to assist humanitarian response in recent years. While organisations already employ UAVs for damage assessment during relief delivery, there is a lack of research into formalising a problem that considers both aspects simultaneously. This paper presents a novel endogenous stochastic vehicle routing problem that coordinates UAV and relief vehicle deployments to minimise overall mission cost. The algorithm considers stochastic damage levels in a transport network, with UAVs surveying the network to determine the actual network damages. Ground vehicles are simultaneously routed based on the information gathered by the UAVs.A case study based on the Haiti road network is solved using a greedy solution approach and an adapted genetic algorithm. Both methods provide a significant improvement in vehicle travel time compared to a deterministic approach and a non-assisted relief delivery operation, demonstrating the benefits of UAV-assisted response.

Perimeter Control of Multiregion Urban Traffic Networks With Time-Varying Delays

In this paper, an adaptive perimeter control problem is studied for urban traffic networks with multiple regions, time-varying state, and input delays. After defining state variables by partition the accumulation variable of each region, a system model is formulated as nonlinear ordinary differential equations based on the concept of macroscopic fundamental diagram. Both the travel times of vehicles as well as evacuation process of traffic jams are first introduced into the system dynamics, and they are modeled as input and state delays, respectively. The control objective is to stabilize the number of vehicles in each region to desired values. By employing the model reference adaptive control scheme and asymptotical sliding mode technique, two filters and adaptive laws for control parameters are designed by using only the information of the reference model. With properly constructed Lyapunov functions, the stability of tracking error with regard to the reference signals is analyzed. Lastly, a simulation example is given to demonstrate the effectiveness of the proposed methods.

Freeway Travel Time Prediction Using Deep Hybrid Model – Taking Sun Yat-Sen Freeway as an Example

As the population keeps growing, traffic congestion happens more and more often. Consequently, travel time has become an important indicator of driving experience. Accurate travel time information helps drivers plan their route more wisely and thus effectively alleviate traffic congestion. In this research, we propose a vehicle travel time prediction model for freeway traffic. The data used in this research are derived from the traffic dataset of the Taiwan Freeway Bureau, and the travel time prediction is made for the Sun Yat-sen Freeway between Taipei and Hsinchu. First, the missing value of the raw data is imputed by Autoencoder. The data are then segmented according to time series and are used to build the prediction model. To effectively capture the hidden features required to predict the travel time for the vehicle traveling on the freeway, a deep learning architecture is adopted in our system, which includes the GRU neural network model, the XGBoost model, and the Hybrid model that combines the GRU and XGBoost through linear regression. To increase computational efficiency, the travel time predictions for consecutive toll gates every 5 minutes apart are pre-computed offline, so that the online travel time prediction of the whole trip can be obtained by simply summing up a few numbers. Experimental results based on actual traffic data show that the proposed system can achieve good performance in terms of prediction accuracy and execution time.

Approximation Algorithms for the Min-Max Cycle Cover Problem With Neighborhoods

In this paper we study the min-max cycle cover problem with neighborhoods, which is to find a given number of $K$ cycles to collaboratively visit $n$ Points of Interest (POIs) in a 2D space such that the length of the longest cycle among the $K$ cycles is minimized. The problem arises from many applications, including employing mobile sinks to collect sensor data in wireless sensor networks (WSNs), dispatching charging vehicles to recharge sensors in rechargeable sensor networks, scheduling Unmanned Aerial Vehicles (UAVs) to monitor disaster areas, etc. For example, consider the application of employing multiple mobile sinks to collect sensor data in WSNs. If some mobile sink has a long data collection tour while the other mobile sinks have short tours, this incurs a long data collection latency of the sensors in the long tour. Existing studies assumed that one vehicle needs to move to the location of a POI to serve it. We however assume that the vehicle is able to serve the POI as long as the vehicle is within the neighborhood area of the POI. One such an example is that a mobile sink in a WSN can receive data from a sensor if it is within the transmission range of the sensor (e.g., within 50 meters). It can be seen that the ignorance of neighborhoods will incur a longer traveling length. On the other hand, most existing studies only took into account the vehicle traveling time but ignore the POI service time. Consequently, although the length of some vehicle tour is short, the total amount of time consumed by a vehicle in the tour is prohibitively long, due to many POIs in the tour. In this paper we first study the min-max cycle cover problem with neighborhoods, by incorporating both neighborhoods and POI service time into consideration. We then propose novel approximation algorithms for the problem, by exploring the combinatorial properties of the problem. We finally evaluate the proposed algorithms via experimental simulations. Experimental results show that the proposed algorithms are promising. Especially, the maximum tour times by the proposed algorithms are only about from 80% to 90% of that by existing algorithms.

A Survey on Deep Reinforcement Learning Network for Traffic Light Cycle Control

A Traffic signal control is a challenging problem and to minimize the travel time of vehicles by coordinating their movements at the road intersections. In recent years traffic signal control systems have on over simplified information and rule-based methods and we have large amounts of data, more computing power and advanced methods to drive the development of intelligent transportation. An intelligent transport system to use the machine learning methods likes reinforcement learning and to explain the acknowledged transportation approaches and a list of recent literature in traffic signal control. In this survey can foster interdisciplinary research on this important topic.

Decentralized Emergency Service Vehicle Pre-Emption System Using RF Communication and GNSS-Based Geo-Fencing

Giving right of way green signal for the Emergency Service Vehicles (ESV) like ambulance and fire service rescue vehicle with minimum delay is a major challenge in Intelligent Transportation Systems (ITS). This challenge is currently achieved by various conventional Emergency Vehicle Pre-emption (EVP) Systems. These conventional systems fail miserably due to false triggering, line of sight issues, network delays, high cost and low reliability. In this paper, we propose a new decentralized Radio Frequency (RF) and Global Navigation Satellite System (GNSS) based Geo-fencing approach which allows smooth passage of ESVs through the junctions with minimum delays. The proposed system was field trailed in one of the junctions in Thiruvananthapuram, India. Pre and post-implementation travel time of emergency vehicles at the junction was evaluated by an external agency National Transportation Planning and Research Centre (NATPAC), Thiruvananthapuram. The evaluation result shows that there is a considerable reduction in the average travel time taken by the emergency vehicles to cross the cordon area in the post-implementation stage as compared to the pre-implementation stage. The reduction in average travel time is approximately 48%.

Traffic Management and Engineering Analysis of the Manahan Flyover Area by using Traffic Micro-Simulation VISSIM

The location of the Manahan railway level crossing is a meeting point for movement between trains and vehicles which often causes traffic congestion and traffic accidents in the area. In connection with the construction of the double track railway, the intensity of traffic congestion in the Manahan railway level crossing area is increasing. In order to overcome traffic congestion in the area, the Surakarta City Government built Manahan Flyover infrastructure. However, due to traffic management and engineering in the existing conditions is not optimal resulting in a relatively bad traffic congestion in Manahan Flyover area. The aim of this study is to conduct traffic management and engineering analysis to minimize traffic congestion in Manahan Flyover area. The traffic micro-simulation VISSIM model is used to evaluate the performance of road networks at peak traffic conditions with different simulation model scenarios. The existing model is built based on a field survey data and then calibrated it to approximate conditions on the field. Then based on the existing model, modelling of several traffic management and engineering scenarios are carried out to obtain the optimal performance of the road network in the Manahan Flyover area. The analysis shows that the simulation model Scenario 3 produces the best performance of road network in terms of vehicle delay, queue length, travel time and speed

Safety Evaluation of Green Light Optimal Speed Advisory (GLOSA) System in Real-World Signalized Intersection

The use of green light optimal speed advisory (GLOSA) systems is seen as a key application for achieving more environmentally friendly, time-efficient, and safer traffic flows at signalized intersections. In previous papers, the authors have proposed a GLOSA system that informs drivers of the most appropriate position for their vehicle instead of their optimal speed. This paper reports on a performance evaluation of our proposed GLOSA system after application to morning rush-hour traffic flow in a simulation of a real-world signalized intersection. A performance evaluation of this numerical simulation showed that our GLOSA system increased the time headway of vehicles and decreased their deceleration rates in the vicinity of the signalized intersection. In addition, the use of the system moderately increased fuel efficiency without affecting vehicle travel time. From these results, it can be concluded that our proposed GLOSA system has the potential to create safer traffic flows in real-world signalized intersections without degrading traffic efficiency.

Modelling the travel time of transit vehicles in real‐time through a GTFS‐based road network using GPS vehicle locations

SummaryPredicting the arrival time of a transit vehicle involves not only knowledge of its current position and schedule adherence, but also traffic conditions along the remainder of the route. Road networks are dynamic and can quickly change from free‐flowing to highly congested, which impacts the arrival time of transit vehicles, particularly buses which often share the road with other vehicles, so reliable predictions need to account for real‐time and future traffic conditions. The first step in this process is to construct a framework with which road state (traffic conditions) can be estimated using real‐time transit vehicle position data. Our proposed framework implements a vehicle model using a particle filter to estimate road travel times, which are used in a second model to estimate real‐time traffic conditions. Although development and testing took place in Auckland, New Zealand, we generalised each component to make the framework compatible with other public transport systems around the world. We demonstrate the real‐time feasibility and performance of our approach in real‐time, where a combination of R and C++ was used to obtain the necessary performance results. Future work will use these estimated traffic conditions in combination with historical data to obtain reliable arrival time predictions of transit vehicles.

A distributed and low-overhead traffic congestion control protocol for vehicular ad hoc networks

The development of new technologies and transportation modes is a crucial component for improving urban mobility. For instance, researchers have shown that vehicular networks are a promising technology to monitor and reduce traffic jams. Nevertheless, most of these solutions rely on costly external infrastructures, such as roadside units or cellular networks. In this work, we propose DisTraC, a protocol for vehicular ad hoc networks. DisTraC is a traffic congestion control protocol of low communication overhead that aims to reduce the average travel time of vehicles by using vehicle-to-vehicle (V2V) communication. The protocol is independent of external infrastructures as uses only V2V communication. Simulation results show that DisTraC outperforms other solutions published in the literature both in terms of communication overhead and capability to reduce traffic congestion.