Travel Time Estimation Model Research Articles

Hybrid travel time estimation model for public transit buses using limited datasets

<p>A reliable transit service can motivate commuters to switch their traveling<br />mode from private to public. Providing necessary information to passengers<br />will reduce the uncertainties encountered during their travel and improve<br />service reliability. This article addresses the challenge of predicting dynamic<br />travel times in urban areas where real-time traffic flow information is<br />unavailable. In this perspective, a hybrid travel time estimation model<br />(HTTEM) is proposed to predict the dynamic travel time using the predicted<br />travel times of the machine learning model and the preceding trip details. The<br />proposed model is validated using the location data of public transit buses of,<br />Tumakuru, India. From the numerical results through error metrics, it is found<br />that HTTEM improves the prediction accuracy, finally, it is concluded that the<br />proposed model is suitable for estimating travel time in urban areas with<br />heterogeneous traffic and limited traffic infrastructure.</p>

Link-Based Traffic Estimation and Simulation for Road Networks Using Electronic Registration Identification Data

Traffic estimation and traffic simulation are essential parts of the intelligent transportation system. In recent years, massive traffic data has brought many data-based traffic estimation methods, but few are utilized in traffic simulation. The cause is that the data-based traffic simulation has high requirements for data quality, needing the trip information of all vehicles, not met by common traffic data. Fortunately, the electronic registration identification (ERI) data of the vehicle can satisfy. Therefore, we utilize ERI data to study the traffic estimation and simulation for the road network. The core of this research is the travel time estimation model, which is constructed by combining traffic theory models and the deep neural network. The traffic theory models are a group of linear models, which represent the relationship between traffic flow or traffic density and travel time. The deep neural network can extract the temporal and spatial characteristics of the road network traffic by Moving Average Convergence-Divergence (MACD) and Graph Convolutional Network (GCN), respectively. We named the travel time estimation model MGCN. Then, we employed MGCN for traffic simulation. In the experiment section, we employed Chongqing ERI data to verify the research content. Compared with some baseline methods, our method is superior in travel time estimation and traffic simulation.

Modeling Road Link-Level Truck Travel Times Using On- and Off-Network Characteristics

Travel times of freight trucks (or trucks) play a major role in trip planning, identification of efficient routes, and allocating resources for implementation of strategies. While research on travel time estimation models for passenger cars or traffic stream is documented in the literature, their applicability for trucks remains debatable. Truck travel is influenced by the road characteristics, surrounding land use, and demographics of an area. The focus of this research, therefore, is on estimating the truck travel times using the on-network (road) and off-network (land use and demographics) characteristics. Truck travel time data for 501 road links in North Carolina from 2019 were processed for four times of the day and two days of the week. Generalized estimating equations (GEE) were used with the average truck travel time per mile (ATTTPM) as the dependent variable. Spatial proximity (buffers of 0.25 mi, 0.50 mi, and 1 mi) and spatial weights (distance decay functions like 1/d, 1/d2, and 1/d3) were explored to check the best possible approach for capturing off-network characteristics. The office (business park and administrative areas), transportation (rest areas and parking facilities), heavy commercial, and light industrial land uses have an increasing influence on the ATTTPM. The linear model developed using data from a 0.25-mi buffer is best suited to estimate the ATTTPM. Off-peak hour delivery incentives or truck priority systems on road links near these land uses can be implemented to improve truck mobility. The methodology illustrated in this research is transferable and could be used for estimating truck travel times across a region.

Context-aware road travel time estimation by coupled tensor decomposition based on trajectory data

Urban road travel time estimation and prediction on a citywide scale is a necessary and important task for recommending optimal travel paths. However, this problem has not yet been well addressed: most existing approaches face serious data sparsity issues, e.g., lack of sensor data on several road segments; and it is a difficult task to capture context patterns around the road and incorporate context-aware information into travel time estimation models. Because of this, we propose to utilize trajectory data to model road travel times as this type of data covers more urban road segments than data from traditional traffic monitoring systems. Moreover, the trajectory itself has involved both travel times and the context of road congestion. A general framework for context-aware road travel time estimation (CARTE) is then put forward. Specifically, we adopt a third-order tensor to model spatiotemporal road travel times by setting the congestion level as a third dimension. By incorporating another context-aware information, namely points of interest (POI), a coupled tensor decomposition algorithm is proposed to fill in missing data. Eventually, we propose an algorithm to calculate an ultimate two-dimensional (spatial and temporal) travel time matrix by weighting the congestion probabilities of each congestion level. The effectiveness of the CARTE was validated on two real-world datasets and it was compared to the state-of-the-art methods. The experimental results demonstrate that the proposed travel time prediction approach always achieves the best performance in terms of accuracy with different data sparsity and prediction horizons.

GraphTTE: Travel Time Estimation Based on Attention-Spatiotemporal Graphs

This letter proposes a new travel time estimation model based on graph neural network (GraphTTE) to improve the accuracy of travel time estimation. We design a Multi-layer Spatiotemporal Graph frame (MSG), which consists of static network and dynamic networks, to fully consider the influence of traffic temporal characteristics and road network topological characteristics on travel time. Moreover, we design an Attention Graph Nodes Impact Index algorithm (AGNII) to score the impact of each node on travel time. In particular, the dynamic networks utilize the graph convolution network and gate recurrent unit to obtain the traffic characteristics, the static network utilizes graph convolution network to obtain the road basic attributes. We combine the real paths sequence with the impact score of nodes to extract the subgraph with a great impact on the trajectory. After the graph representation learning and deep residual network, the estimated time is obtained. A simulator was designed to train and test our model in Chengdu and Xi'an datasets, the results show that the mean absolute percent error (MAPE) is 12.58% and 14.01%, which is 1.54% and 1.78% lower than the baselines.

Freeway travel time estimation based on the general motors model: a genetic algorithm calibration framework

Travel time estimation plays an important role in freeway performance assessment and reliability management. Conventional estimation methods based on speed information do not consider the level of congestion during modelling, leading to unreliable estimations under congested conditions. This study presents a freeway travel time estimation method based on the general motors (GM) model, where the concept ‘virtual vehicle’ was proposed to apply macroscopic traffic flow data to the microscopic GM model. First, a GM-based travel time estimation (GMTTE) model was developed to estimate link travel time. Three sets of parameters l , m and α were separately used under free-flowing, congestion and transition conditions. Next, two corridor estimation models, namely GMTTE time-slice-based (GMTTE-TSB) model and GMTTE continuous-speed (GMTTE-CS) model, were proposed. To calibrate the three parameters, a genetic algorithm framework was developed. The proposed method was calibrated and evaluated using traffic data collected from seven freeway segments in the greater St. Louis, Missouri region. Results show that (i) the GMTTE-CS model outperformed the GMTTE-TSB model; (ii) the optimal parameters of the GMTTE-CS model under free-flowing and congested conditions are l = 1.1 , m = 2.0 , α = 8.0 and l = 1 , m = 0.1 , α = 8.0 , respectively; (iii) the GMTTE-CS model outperformed the instantaneous and time-slice models under congested conditions, while showing similar accuracies with the two models under free-flowing and status-transition conditions; and (iv) the optimal time interval was found to be 9–10 min for free-flowing conditions and 6–7 min for congested conditions.

Developing a Travel Time Estimation Method of Freeway Based on Floating Car Using Random Forests

Travel time of traffic flow is the basis of traffic guidance. To improve the estimation accuracy, a travel time estimation model based on Random Forests is proposed. 7 influence variables are viewed as candidates in this paper. Data obtained from VISSIM simulation are used to verify the model. Different from other machine learning algorithm as black boxes, Random Forests can provide interpretable results through variable importance. The result of variable importance shows that mean travel time of floating car t-f, traffic state parameter X, density of vehicle Kall, and median travel time of floating car tmenf are important variables affecting travel time of traffic flow; meanwhile other variables also have a certain influence on travel time. Compared with the BP (Back Propagation) neural network model and the quadratic polynomial regression model, the proposed Random Forests model is more accurate, and the variables contained in the model are more abundant.

Modelling Travel Time After Incidents on Freeway Segments in China

The reduction in incident-induced delays on freeways is a main objective of transportation management. The use of travel time estimation model for freeway segments is an important method for estimating delays resulting from incidents on freeways. In this study, freeways with temporary partial lane closures were considered to simulate traffic accidents occupying lanes. Travel time, traffic volumes, and speeds under various traffic conditions on a few typical Chinese freeway segments under regular and simulated accident conditions were investigated through field experiments. The collected traffic data collected were used to establish travel time models based on the Bureau of Public Roads (BPR) function for basic freeway segments under both regular and accident conditions, and to obtain the model parameters. The results demonstrate that the calibrated BPR models established in this study fit the data well. In addition, this study proposes an application method for the established travel time models by which variations in travel time can be estimated rapidly and easily. The results of this study can be used to reduce travel time for road users and contribute to decision making of transportation management systems to improve traffic efficiency after incidents.

Arterial link travel time estimation considering traffic signal delays using cellular handoff data

Link travel time is an important evaluation index of urban arterial traffic condition. It is traditionally estimated from loop detector data and GPS probe vehicle data. The former suffers from the coverage and maintenance issues. The latter has issues related to low penetration rate. Cellphone handoff data is one alternative that may address those deficiencies with its large coverage areas and continuous detection capabilities at low cost. Existing cellular probe vehicle based travel time estimation methods are primarily designed for freeway. Limited studies have been conducted for signalised arterials. Cellular handoff data detects the time lapse between two consecutive wireless handoff points. When vehicles with on-call cellphones traverse between two handoff points, their space mean speeds can be calculated by dividing the pre-calibrated distances by the time lapses. In this study, a new cellular handoff data based arterial travel time estimation model is proposed and compared with existing models. A hybrid traffic-and-wireless simulation network is built to simulate the handoff data generation and processing. Field data are collected at an arterial corridor in Chengdu, China to establish the baseline condition. The results from the proposed model show promising performance with detection errors of link travel times mostly within 10%.

Modified Bayesian data fusion model for travel time estimation considering spurious data and traffic conditions

This paper presents a framework for the development of the travel time estimation model using multiple sources of data with consideration of spurious data and traffic conditions. A modified Bayesian data fusion approach, combined with the Gaussian mixture model, is used to fuse the travel time data, which are estimated from different types of sensors to improve accuracy, precision, as well as completeness of data, in terms of spatial and temporal distribution. Two additional features are added into existing models including the difference of traffic conditions classified by the Gaussian mixture model and the bias estimation from individual sensor by introducing a non-zero mean Gaussian distribution which learned from the training dataset. The methodology and computational procedure are presented. The Gaussian mixture model is used to classify states of traffic into predefined number of traffic regimes. Once a traffic condition is classified, the modified Bayesian data fusion approach is used to estimate travel time. The proposed model provides explicit advantages over the basic Bayesian approach, such as being robust to noisy data, reducing biases of an individual estimation, and producing a more precise estimation of travel time. Two different real-world datasets and one simulated dataset are used to evaluate the performance of the proposed model under three different traffic regimes: free flow, transitional flow and congested flow regimes. The results when compared with the results from benchmark models show significant improvement in the accuracy of travel time estimation in terms of mean absolute percentage errors (MAPE) in the range of 3.46% to 16.3%.

Travel Time Estimation Modelling under Heterogeneous Traffic: A Case Study of Urban Traffic Corridor in Surat, India

Achievement of fast and reliable travel time on urban road network is one of the major objectives for a transport planner against the enormous growth in vehicle population and urban traffic in most of the metropolitan cities in India. Urban arterials or main city corridors are subjected to heavy traffic flow resulting in degradation of traffic quality in terms of vehicular delays and increase in travel time. Since the Indian roadway traffic is characterized by heterogeneity with dominance of 2Ws (Two wheelers) and 3Ws (Auto rickshaw), travel times are varying significantly. With this in background, the present paper focuses on identification of travel time attributes such as heterogeneous traffic, road side friction and corridor intersections for recurrent traffic condition and to develop an appropriate Corridor Travel Time Estimation Model using Multi-Linear Regression (MLR) approach. The model is further subjected to sensitivity analysis with reference to identified attributes to realize the impact of the identified attributes on travel time so as to suggest certain measures for improvement.

Time-dependent trip-chain link travel time estimation model with the first-in–first-out constraint

Estimating network-wide time-dependent link travel time is a key problem in transportation management. During the examination of users’ time-dependent route choice behaviors, the conditions and changes in traffic flow propagation in time–space networks must satisfy the first-in–first-out principle in order to be applicable for transportation planning. In this context, we developed a bilevel programming model with the first-in–first-out constraint for estimating time-dependent trip-chain link travel times under to examine users’ trip-chain route choice behaviors. Subsequently, we derived a bilevel mathematical formulation and developed an iterative algorithm based on Lagrangian gradient projection. Several numerical examples are presented herein to explain the accuracy of the model. Finally, conclusions and suggestions are presented on the basis of the research findings.

Estimating freeway travel time and its reliability using radar sensor data

ABSTRACTTravel time and its reliability are intuitive system performance measures for freeway traffic operations. This paper proposes a method to estimate travel times based on data collected from roadside radar sensors, considering spatially correlated traffic conditions. Link-level and corridor-level travel time distributions are estimated using these travel time estimates and compared with the ones estimated based on probe vehicle data. The maximum likelihood estimation is used to estimate the parameters of Weibull, gamma, normal, and lognormal distributions. According to the log-likelihood values, lognormal distribution is the best fit among all the tested distributions. Corridor-level travel time reliability measures are extracted from the travel time distributions. The proposed travel time estimation model can well capture the temporal pattern of travel time and its distribution.

Comparison of travel time measurement methods along freeway and arterial facilities

Travel time is an important performance measure used to assess traffic operational quality of various types of facilities. Previous efforts to estimate travel time have also compared model-estimated travel times to field-measured travel times using various sources of data. Given the variety and diversity of travel time measurement methods, it is important to evaluate the accuracy of the data obtained by each of them and to develop recommendations regarding their suitability in the validation of travel time estimation models. The research objective for this paper was to collect field data along several freeways and arterials and to evaluate the travel times obtained by STEWARD (Statewide Traffic Engineering Warehouse for Regionally Archived Data), INRIX, BlueTOAD (Bluetooth Travel-time Origination and Destination), and HERE. Benchmark data were collected with the use of an instrumented vehicle at five freeway segments and two arterial segments in Florida. The field-measured travel times were statistically compared with the travel times provided through various methods. The results suggest that the HERE traffic data provide better freeway travel time estimates compared to the remaining methods. In oversaturated conditions, STEWARD, INRIX and BlueTOAD data seem to underestimate travel times, while HERE data were found to be more accurate. For undersaturated freeways, STEWARD, INRIX and BlueTOAD were found to perform better than HERE. At the arterial sites BlueTOAD and HERE travel time data were analyzed and the analysis suggests that none of the methods is accurate, possibly due to the small sample size.

Travel time estimation method for urban road based on traffic stream directions

ABSTRACTTraffic flow on a given link in an urban road network can be divided into several traffic streams, depending on their turning manoeuvres when entering and leaving the link. These traffic streams may experience various travel times due to multiple reasons, such as fluctuations in traffic demand/supply and stochastic arrivals/departures at signalised intersections. However, the current travel time estimation methods take traffic flow as a whole and produce a single estimation value. This approach can produce large errors. Furthermore, given the travel time information of each traffic stream, the results of dynamic traffic assignment models can be made much more accurate and the effect of signal controls improved. In this paper, a comparison analysis is conducted to verify the significant difference in link travel times of different traffic streams. Then, link travel time is redefined in consideration of traffic stream directions. This process is also successful in filtering noise, as shown in the numerical experiments. In addition, existing estimation methods cannot reflect real values or fluctuations of travel times in sampling intervals without any valid observational data. To solve this problem, a regression model is built and integrated into the travel time estimation model. Error analysis of several links on two different days demonstrates the improvements made by the model.

An intersection-to-intersection travel time estimation and route suggestion approach using vehicular ad-hoc network

Estimation of time-dependent travel time in an urban network is a challenging task due to the interrupted nature of vehicular traffic flows. A novel concept of intersection-to-intersection (I2I) real-time travel time estimation (TTE) and route suggestion model based on vehicular ad-hoc network (VANET) technology is proposed for enabling the smart transportations in smart city. The system components, communication protocol and collaborative intelligence concept are designed to facilitate real-time vehicular applications. Vehicles equipped with an on-board unit (OBU) send TTE requests to the road side unit (RSU) and share their real-time information, including traveled path and average speed, and the RSU responds with the suggested shortest route as well as the estimated travel time. In order to efficiently share real-time traffic information among RSUs, a propagation-based RSU-to-RSU (R2R) data exchange algorithm and a traffic information super-matrix data structure are designed. These reduce the complexity from O(N2) for a traditional broadcast approach to O(N). Real data collected from a GPS-based taxi dispatching system is applied to evaluate the accuracy of the proposed TTE model and the performance of the suggested route. The experimental results show that the average mean absolute error percentage (MAPE) of the proposed TTE model is 13.6% compared to real taxi journeys, which indicates that the performances of the suggested routes are good. The TTE of the suggested paths has the possibility of being 82.2% better than the paths traveled by taxi, and the travel time is thus reduced by 15.9% on average over a year.

Estimating Freeway Travel Times Using the General Motors Model

Travel time is a key transportation performance measure because of its diverse applications. Various modeling approaches to estimating freeway travel time have been well developed due to widespread installation of intelligent transportation system sensors. However, estimating accurate travel time using existing freeway travel time models is still challenging under congested conditions. Therefore, this study aimed to develop an innovative freeway travel time estimation model based on the General Motors (GM) car-following model. Since the GM model is usually used in a microsimulation environment, the concepts of virtual leading and virtual following vehicles are proposed to allow the GM model to be used in macroscale environments using aggregated traffic sensor data. Travel time data collected from three study corridors on I-270 in Saint Louis, Missouri, were used to verify the estimated travel times produced by the proposed General Motors travel time estimation (GMTTE) model and two existing models, the instantaneous model and the time-slice model. The results showed that the GMTTE model outperformed the two existing models due to lower mean average percentage errors of 1.62% in free-flow conditions and 6.66% in two congested conditions. Overall, the GMTTE model demonstrated its robustness and accuracy for estimating freeway travel times.

A Bayesian mixture model for short-term average link travel time estimation using large-scale limited information trip-based data

Accurate estimation and prediction of urban link travel times are important for urban traffic operations and management. This paper develops a Bayesian mixture model to estimate short-term average urban link travel times using large-scale trip-based data with partial information. Unlike typical GPS trajectory data, trip-based data from taxies or other sources provide limited trip level information, which only contains the trip origin and destination locations, trip travel times and distances, etc. The focus of this study is to develop a robust probabilistic short-term average link travel time estimation model and demonstrate the feasibility of estimating network conditions using large-scale trip level information. In the model, the path taken by each trip is considered as latent and modeled using a multinomial logit distribution. The observed trip data given the possible path set and the mean and variance of the average link travel times can thus be characterized using a finite mixture distribution. A transition model is also introduced to serve as an informative prior that captures the temporal and spatial dependencies of link travel times. A solution approach based on the expectation–maximization (EM) algorithm is proposed to solve the problem. The model is tested on estimating the mean and variance of the average link travel times for 30 min time intervals using a large-scale taxi trip dataset from New York City. More robust estimation results are obtained owing to the adoption of the Bayesian framework.

Travel Time Estimation Based on Fused Traffic State Data: Case Studies in US and South Korea

The goal of Advance Traveler Information System (ATIS) is to improve traffic flow and safety by providing up-to-date information of traffic network. In this paper, traffic information (travel time) estimation based on fused traffic state data is presented. A centralized architecture is used to fuse the traffic sate data from different sensors based fusion by averaging and fusion by median and to estimate the travel time based on the simultaneous travel time estimation model accordingly. Two case studies are selected to investigate the performance of fusion models based on freeway data in the United States of America (USA) and the arterial road data in South Korea. The results show that the fusion by median performs best. The model is able to eliminate outliers in the data with less effort of complex mathematical process. It can be used as a benchmark for comparison with other advanced fusion models.

Simulation-Based Sensor Location Model for Arterial Street

Traffic sensors serve as an important way to a number of intelligent transportation system applications which rely heavily on real-time data. However, traffic sensors are costly. Therefore, it is necessary to optimize sensor placement to maximize various benefits. Arterial street traffic is highly dynamic and the movement of vehicles is disturbed by signals and irregular vehicle maneuver. It is challenging to estimate the arterial street travel time with limited sensors. In order to solve the problem, the paper presents travel time estimation models that rely on speed data collected by sensor. The relationship between sensor position and vehicle trajectory in single link is investigated. A sensor location model in signalized arterial is proposed to find the optimal sensor placement with the minimum estimation error of arterial travel time. Numerical experiments are conducted in 3 conditions: synchronized traffic signals, green wave traffic signals, and vehicle-actuated signals. The results indicate that the sensors should not be placed in vehicle queuing area. Intersection stop line is an ideal sensor position. There is not any fixed sensor position that can cope with all traffic conditions.