Link Travel Speed Research Articles

Fleet sizing and charging infrastructure design for electric autonomous mobility-on-demand systems with endogenous congestion and limited link space

Autonomous vehicles are to revolutionize the way urban mobility demands are served, and they are most likely to be powered by electricity. To accurately quantify the benefits of replacing existing mobility services with autonomous electric vehicles, electric autonomous mobility-on-demand (EAMoD) systems need to be evaluated and designed with the congestion effects they may cause taken into account. In this work, the congestion effects are depicted through a discrete event simulation model with state-dependent link travel speed and limited link space that allow endogenous congestion to emerge, spread, and dissipate across the entire road network. Three mathematical models are integrated into the simulation model to optimally match vehicles with waiting requests, relocate empty vehicles to potential high-demand areas, and assign low charge vehicles to charging stations based on the workloads of the charging stations. Based on the simulation model, we apply Bayesian optimization to jointly design the fleet size and charging facility configuration considering the endogenous congestion incurred by the daily operation of autonomous electric vehicles. Contraction hierarchies are adopted to route vehicles to perform assigned tasks in real-time. The proposed solution method is tested on Manhattan below 60th street, which corresponds to the potential congestion pricing zone in New York City. Experiment results show that the proposed simulation-based optimization approach is computationally tractable, and can find a satisfactory solution to the fleet sizing and charging infrastructure design problem within a tight computation budget. Excluding congestion effects at system design stage would lead to up to 14% passenger loss due to long assignment waiting time. Although the charging facility cost accounts for only 1.8% of the total cost, the number of chargers can indirectly affect the percentage of passengers that can be served through its efficiency in recharging vehicles. A charging facility configuration aligned with the fleet size can help to improve service quality and vehicle utilization rate.

Cooperative multi-camera vehicle tracking and traffic surveillance with edge artificial intelligence and representation learning

Traffic surveillance cameras are the eyes of the Intelligent Transportation Systems (ITS). However, they are currently isolated and can only extract information from each of their fixed views. To track vehicles across multiple cameras and help public agencies collect link travel time and speed information, an Edge-empowered Cooperative Multi-camera Sensing (ECoMS) System is proposed. ECoMS system presents a novel algorithmic and edge-server cooperative system construct to push edge computing and multi-camera re-identification workflow serving for traffic sensing based on Internet of Things (IoT) architecture. On the algorithm side, ECoMS system proposes a featherlight edge-based computer vision framework for vehicle detection, tracking, and features selection process in a real-time manner. Then, by only sending the objects’ representations to the server, the high-bandwidth data transmission and the heavy post-processing system can be abandoned. Furthermore, a hierarchical clip-based deep vehicle re-identification framework is proposed and integrated into the ECoMS system, and significantly outperforms other state-of-the-art methods by 4%–8% on Rank-1 accuracy. Finally, to balance the accuracy level of different camera pairs, a collaborative cross-camera traffic information estimation framework based on kernel density estimation with kernel smoother is implemented, which can get the precise link and region traffic information together with distributions (less than 1.01 KL distance). By maximizing the cooperation of the computational resources, orchestrating the data transmission and integrating road network graph features in the system, the ECoMS can precisely model the network-scale traffic information in a flexible, cost-effective, and scalable workflow. To the author’s best knowledge, ECoMS is the first multi-camera vehicle tracking and traffic monitoring system based on cooperative IoT architecture.

A reliable energy consumption path finding algorithm for electric vehicles considering the correlated link travel speeds and waiting times at signalized intersections

Electric vehicles are an essential part of the urban green transportation network, with their popularization and application being of great significance to the sustainable development of cities. In recent years, electric vehicles have continued to increase their merit in the vehicle fleet, yet they are restricted by battery capacity and the availability of charging stations. Proper path planning for electric vehicles is conducive to improving the utilization rate of road resources and easing traffic congestion, and is of great significance to the vehicles’ energy utilization rate and reducing greenhouse gas emissions. Hence, this paper deals with a reliable energy consumption path finding (RECPF) algorithm for signalized traffic networks with electric vehicles in the presence of uncertainty. The RECPF problem is formulated as the probability of completing a trip without exhausting a given battery energy budget. Moreover, we extend RECPF models accounting for the link travel speed correlations and delays at signalized intersections. Addressing the non-additivity and nonlinearity of the suggested optimization model, we propose a modified heuristic algorithm based on the Dijkstra and k shortest path algorithms that solve the RECPF problems. The established algorithm’s efficiency and usefulness are demonstrated on the grid-based road network of Hong Kong, revealing that incorporating link travel speed correlations and delays at signalized intersections enhances the electric vehicle’s energy consumption prediction accuracy and produces a more efficient, optimal, and reliable path recommendations.

A Reliable Energy Consumption Path - Finding Algorithm for Signalized Traffic Networks with Electric Vehicles Considering the Correlated Link Travel Speeds

A Reliable Energy Consumption Path - Finding Algorithm for Signalized Traffic Networks with Electric Vehicles Considering the Correlated Link Travel Speeds

Evaluation of travel speed of conventional buses and bus rapid transit service in Ahmedabad city, India using geo-informatics

• GPS data logged by transit vehicles was used to determine travel speed. • Assessed spatio-temporal variability in travel speed of conventional buses and BRTS. • Identified congestion hot spots using Getis-Ord G i * statistics. • BRTS in Ahmedabad city was found 4 km/h faster than the conventional buses. • Travel Speed of BRTS declined to 13 km/h during peak-hours in inner city. Bus Rapid Transit Service (BRTS) attempts to overcome the limitations of conventional bus service with lane separation and higher operational frequency. The policy-makers are frequently required to quantitatively evaluate the improvement in quality of transportation offered by BRTS with respect to that of conventional buses to not only expand the network in newer areas within the city, but also to implement the same in other similar cities. This paper demonstrates an approach based on the Global Positioning Systems (GPS) data logged by transit vehicles in assessing the performance of conventional buses and BRTS in Ahmedabad city, India. The link travel speed derived from GPS data provides an insight into the spatio-temporal variability in conventional buses and BRTS. The congestion hot spots indicating high and low values of travel speed in conventional buses and BRTS were identified using the hot spot analysis (Getis-Ord G i * statistics). The study shows that the BRTS network did provide higher travel speed at lower variability over conventional buses in the study area. However, BRTS provided no improvement in travel speed in the densely populated inner city regions during peak hours, offering little advantage of lane separation. The study also shows the utility of voluminous GPS data logged by transit vehicles that is otherwise lying dormant with transit operators for identifying traffic hot spots within the city.

Travel Time Comparison Using Google Map and OSM Tracker

In India, people’s socio-economic characteristics in society have changed over the decades which have resulted in increase of vehicle ownership at a huge rate which poses a lot of problem in both traffic control and management system. In the area where continuation is impossible, congestion can be overcome by managing and controlling the traffic. There are major developments in ITS (Intelligent Transportation System) which needs travel time as a major input. Measure the quality of any transportation network travel time is a very popular parameter. The estimation of travel time in urban network became more difficult because of the rapid change in the system and traffic. Emerging technologies such as GPS which become widely available within the last decade, can offer promising improvements in traffic monitoring technologies. Probe vehicles equipped with relatively cheap devices provide researchers with vast amounts of disaggregate traffic data that can be easily used for determining link travel speeds. This study is done in order to estimate the impact of different travel modes on travel time with different day. Data has been collected in a study stretch of 8.1 km length in Khardaha to Dunlop more, KOLKATA using mobile GPS application (OSM tracker in android Operating System) . Different private modes of transportation such as motor bike, bus and car have been used as test vehicle for the collection of data in different traffic flow scenarios in different times and also days. Latitude, longitude, speed and the travelled path at one second interval has been extracted from the GPS application. After completing a data collection, data extracted from the Osm Tracker that is GPX file to convert the value into Excel format. The travel time and average speed of different modes has been compared and the effect of vehicle composition on travel time and average speed has been analyzed.

Critical Segments Identification for Link Travel Speed Prediction in Urban Road Network

Predicting traffic operational condition is crucial to urban transportation planning and management. A large variety of algorithms were proposed to improve the prediction accuracy. However, these studies were mainly based on complete data and did not discuss the vulnerability of massive data missing. And applications of these algorithms were in high-cost under the constraints of high quality of traffic data collecting in real-time on the large-scale road networks. This paper aims to deduce the traffic operational conditions of the road network with a small number of critical segments based on taxi GPS data in Xi’an city of China. To identify these critical segments, we assume that the states of floating cars within different road segments are correlative and mutually representative and design a heuristic algorithm utilizing the attention mechanism embedding in the graph neural network (GNN). The results show that the designed model achieves a high accuracy compared to the conventional method using only two critical segments which account for 2.7% in the road networks. The proposed method is cost-efficient which generates the critical segments scheme that reduces the cost of traffic information collection greatly and is more sensible without the demand for extremely high prediction accuracy. Our research has a guiding significance on cost saving of various information acquisition techniques such as route planning of floating car or sensors layout.

Understanding the marginal distributions and correlations of link travel speeds in road networks

Link travel speeds in road networks are essential data for a variety of research problems in logistics, transportation, and traffic management. Real-world link travel speeds are stochastic, and highly dependent on speeds in previous time periods and neighboring road links. To understand how link travel speeds vary over space and time, we uncover their distributions, their space- and/or time-dependent correlations, as well as partial correlations, based on link travel speed datasets from an urban road network and a freeway network. We find that more than 90% (57%) of travel speeds are normally distributed in the urban road (freeway) network, and that correlations generally decrease with increased distance in time and space. We also investigate if and how different types of road links affect marginal distributions and correlations. The results show that different road link types produce quite similar marginal distributions and correlations. Finally, we study marginal distributions and correlations in a freeway network. Except that the marginal distribution and time correlation are different from the urban road network, others are similar.

Imputation Model of the Link Travel Speed Data for Incident Detection System

Imputation Model of the Link Travel Speed Data for Incident Detection System

A Hybrid Approach Based on Variational Mode Decomposition for Analyzing and Predicting Urban Travel Speed

Predicting travel speeds on urban road networks is a challenging subject due to its uncertainty stemming from travel demand, geometric condition, traffic signals, and other exogenous factors. This uncertainty appears as nonlinearity, nonstationarity, and volatility in traffic data, and it also creates a spatiotemporal heterogeneity of link travel speed by interacting with neighbor links. In this study, we propose a hybrid model using variational mode decomposition (VMD) to investigate and mitigate the uncertainty of urban travel speeds. The VMD allows the travel speed data to be divided into orthogonal and oscillatory sub-signals, called modes. The regular components are extracted as the low-frequency modes, and the irregular components presenting uncertainty are transformed into a combination of modes, which is more predictable than the original uncertainty. For the prediction, the VMD decomposes the travel speed data into modes, and these modes are predicted and summed to represent the predicted travel speed. The evaluation results on urban road networks show that, the proposed hybrid model outperforms the benchmark models both in the congested and in the overall conditions. The improvement in performance increases significantly over specific link-days, which generally are hard to predict. To explain the significant variance of the prediction performance according to each link and each day, the correlation analysis between the properties of modes and the performance of the model are conducted. The results on correlation analysis show that the more variance of nondaily pattern is explained through the modes, the easier it was to predict the speed. Based on the results, discussions on the interpretation on the correlation analysis and future research are presented.

Spatiotemporal Congestion Recognition Index to Evaluate Performance under Oversaturated Conditions

We developed a congestion index for traffic management that uses link travel time based on global positioning system probe vehicle data. The congestion index can be used to evaluate the performance of an arterial network under oversaturated conditions. It considers congestion intensity and congestion duration, unlike existing congestion indices. After investigating current big traffic data, we selected the average link travel speed that could be collected at all times over a wide range to develop the congestion index. We used extra travel time as a new measure of effectiveness. The congestion criterion for monitoring oversaturated conditions was set as extra travel time with three congestion levels when traffic flow exceeded capacity. We devised the new congestion index by reflecting the three congestion levels to monitor oversaturated conditions macroscopically and to examine congestion characteristics microscopically. A spatiotemporal congestion recognition index (SCRIN) was defined based on congestion intensity and congestion duration. We analyzed SCRIN to monitor the traffic congestion of Seoul city using the data on travel speeds from the seoul transport operation and information service (TOPIS) for 2016.

Identification of Spatiotemporal Relationships in Travel Speeds along Individual Roadways using Probe Vehicle Data

The existence of spatiotemporal correlations in traffic behavior on links in a transportation network is potentially very useful. However, traffic metrics are often strongly correlated simply because of natural variations in travel demand patterns and these temporal trends might obstruct more meaningful relationships caused by the physics of traffic. To overcome this challenge, the present paper proposes a non-parametric, moving average detrending method that can be used to remove these background trends, even during non-stationary periods in which traffic states are changing with time. Cross-correlations performed on the detrended data are then used to identify more meaningful trends. The proposed method can also incorporate temporal lags in correlations between individual links, which accounts for the time it takes for information to travel between them. Links that exhibit strong correlations after detrending can then be grouped into communities which behave together using graph theory methods, and this community structure can be leveraged to improve prediction of link performance when information is missing. The proposed methodology is applied to a case study network using real-time link travel speeds obtained from probe vehicles. The results reveal that the 40 links in the network can be grouped into between eight and 12 communities, depending on the day of the week. This suggests that only a handful of links may need to be monitored to estimate travel speeds across the entire network. Furthermore, the significant overlap in the community structure across these days reveals that the network structure plays a large role in spatiotemporal correlations in link travel speeds in a network.

Urban link travel speed dataset from a megacity road network

Link travel speeds in road networks are fundamental data in many research areas of traffic, transportation, and logistics. To support the research in these areas, we develop a dataset, containing the travel speeds on each road link and in different time periods together with the real road network map. The dataset is collected from a representative megacity in Western China, Chengdu. The road network of this city involves different urban road network structures. The dataset shows the realistic variations and randomness of urban link travel speeds. This enables the research of real data-driven decision-making problems in traffic, transportation and logistics areas.

Travel time predictions: should one model speeds or travel times?

PurposeTravel time predictions are of importance for individual trip planning as well as for logistics applications. Since travel time and travel speed have a one-one correspondence, the modeller has the choice to model travel times directly or model the corresponding travel speeds and infer the associated time from the speed predictions. A priori it is not clear which of these is the superior approach. In this paper we investigate the implications of the choice of the methodology for the accuracy of the travel time predictions.MethodsFor a selection of links, travel time prediction models, both in a direct way as well as indirectly via the implied link travel speeds, are obtained. The respective predictions are compared on a validation data set with respect to their accuracy as measured by mean error, root mean square error, mean percentage error as well as mean absolute percentage error. Additionally, the accuracy of route travel time predictions is evaluated based on the raw GPS data from the floating taxis.ResultsThe empirical results overwhelmingly make the case for using direct modelling if the goal of prediction is to obtain a RMSE-optimal prediction. If the MAPE is to be minimized, however, the indirect method provides the better results.ConclusionThus the goal of the prediction determines the better method of modelling: if one is interested in minimizing the RMSE, then, for the data investigated in this paper, the direct method should be selected. However, if one is interested in obtaining a small MAPE, the indirect method achieves better results.

An Improved Shuffled Frog Leaping Algorithm and Its Application in Dynamic Emergency Vehicle Dispatching

The traditional method for solving the dynamic emergency vehicle dispatching problem can only get a local optimal strategy in each horizon. In order to obtain the dispatching strategy that can better respond to changes in road conditions during the whole dispatching process, the real-time and time-dependent link travel speeds are fused, and a time-dependent polygonal-shaped link travel speed function is set up to simulate the predictable changes in road conditions. Response times, accident severity, and accident time windows are taken as key factors to build an emergency vehicle dispatching model integrating dynamic emergency vehicle routing and selection. For the unpredictable changes in road conditions caused by accidents, the dispatching strategy is adjusted based on the real-time link travel speed. In order to solve the dynamic emergency vehicle dispatching model, an improved shuffled frog leaping algorithm (ISFLA) is proposed. The global search of the improved algorithm uses the probability model of estimation of distribution algorithm to avoid the partial optimal solution. Based on the Beijing expressway network, the efficacy of the model and the improved algorithm were tested from three aspects. The results have shown the following: (1) Compared with SFLA, the optimization performance of ISFLA is getting better and better with the increase of the number of decision variables. When the possible emergency vehicle selection strategies are 815, the objective function value of optimal selection strategies obtained by the base algorithm is 210.10% larger than that of ISFLA. (2) The prediction error of the travel speed affects the accuracy of the initial emergency vehicle dispatching. The prediction error of ±10 can basically meet the requirements of the initial dispatching. (3) The adjustment of emergency vehicle dispatching strategy can successfully bypassed road sections affected by accidents and shorten the response time.

Evaluating roadway network efficiency using archived link travel speed data

ABSTRACTAn evaluation of the roadway network efficiency is typically conducted by applying the two-fluid model, a macroscopic traffic flow model. This model generally requires two types of travel time data – running and stop times – for links, and these data are usually obtained by deploying a small number of test cars. Unlike this practice, this study utilizes archived link travel speed data which do not contain running and stop times. Because of this aspect, this study suggests an analytical framework, including a simple process of estimating running and stop times from average link travel speed. In addition, of the two two-fluid model parameters, only one parameter n is used for comparison purposes by fixing the other parameter Tm. A case study using Seoul network indicates that the network efficiency varies across the metropolitan area and that the network efficiency of core areas is superior to that of the other areas.

Arterial Speed Studies Based on Data from GPS Equipped Probe Vehicle

Due to more complicated behavior than controlled access roadway, congested estimation on arterial road network has become a challenging topic for all traffic professionals in recent years. To indicate the traffic condition, link travel speed is considered as one of the important markers for representing traffic status on roadway network and most understood pointer for all road users. This paper describes the implementation of Running Speed and Stopped Delay (RSSD) technique for investigating the urban link travel speed and discusses on the limitation of error in speed associated from each global positioning system (GPS) device to maintain the advantages of proposed technique in travel speed estimation. The results from real observation traffic data on urban roadway also confirm that the accuracy of travel speed estimation is significantly improved when RSSD was employed as the estimation technique compared to baseline approach particularly in the links with highly congested condition.

Shortest path and vehicle trajectory aided map-matching for low frequency GPS data

Map-matching algorithms that utilise road segment connectivity along with other data (i.e. position, speed and heading) in the process of map-matching are normally suitable for high frequency (1Hz or higher) positioning data from GPS. While applying such map-matching algorithms to low frequency data (such as data from a fleet of private cars, buses or light duty vehicles or smartphones), the performance of these algorithms reduces to in the region of 70% in terms of correct link identification, especially in urban and sub-urban road networks. This level of performance may be insufficient for some real-time Intelligent Transport System (ITS) applications and services such as estimating link travel time and speed from low frequency GPS data. Therefore, this paper develops a new weight-based shortest path and vehicle trajectory aided map-matching (stMM) algorithm that enhances the map-matching of low frequency positioning data on a road map. The well-known A∗ search algorithm is employed to derive the shortest path between two points while taking into account both link connectivity and turn restrictions at junctions. In the developed stMM algorithm, two additional weights related to the shortest path and vehicle trajectory are considered: one shortest path-based weight is related to the distance along the shortest path and the distance along the vehicle trajectory, while the other is associated with the heading difference of the vehicle trajectory.The developed stMM algorithm is tested using a series of real-world datasets of varying frequencies (i.e. 1s, 5s, 30s, 60s sampling intervals). A high-accuracy integrated navigation system (a high-grade inertial navigation system and a carrier-phase GPS receiver) is used to measure the accuracy of the developed algorithm. The results suggest that the algorithm identifies 98.9% of the links correctly for every 30s GPS data. Omitting the information from the shortest path and vehicle trajectory, the accuracy of the algorithm reduces to about 73% in terms of correct link identification. The algorithm can process on average 50 positioning fixes per second making it suitable for real-time ITS applications and services.

Factors Impacting Link Travel Speed Reliability: A Case Study at Cairo, Egypt

Factors Impacting Link Travel Speed Reliability: A Case Study at Cairo, Egypt

Real‐time estimation of travel speed using urban traffic information system and filtering algorithm

Travel speed is an important parameter for measuring road traffic. urban traffic information system (UTIS) was developed as a mobile detector for measuring link travel speeds in South Korea. However, UTIS incur errors, such as those caused by irregular vehicle trajectories and communication delays. This study describes an algorithm developed for estimating reliable and accurate average roadway link travel speeds using UTIS data. The algorithm estimates link travel times using a robust data-filtering procedure to identify valid observations within a sampling interval using a varying data validity window. The size of the data validity window varies as a function of the standard deviation of observations in previous intervals. A field test showed that the variance of the percent errors of link travel times was reduced when measured using the new model. Therefore it can be concluded that the proposed model significantly improves travel speed measuring accuracy.