Accuracy Of Travel Time Estimation Research Articles

Estimating Toll Road Travel Times Using Segment-Based Data Imputation

Efficient and sustainable transportation is crucial for addressing the environmental and social challenges associated with urban mobility. Accurate estimation of travel time plays a pivotal role in traffic management and trip planning. This study focused on leveraging machine learning models to enhance travel time estimation accuracy on toll roads under diverse traffic conditions. Two models were developed for travel time estimation under a variety of traffic conditions on the Don Muang Tollway, Bangkok, Thailand: a long short-term memory (LSTM) recurrent neural network model and a support vector regression (SVR) model. Missing data were treated using the proposed segment-based data imputation method. Unlike other studies, the effects of missing input data on the travel time model performance were also analyzed. Traffic parameters, such as speed and flow, along with other relevant parameters (time of day, day of the week, holiday indicators, and a missing data indicator), were fed into each model to estimate travel time on each of the four specific routes. The LSTM and SVR results had similar performance levels based on evaluating the all-day pooled data. However, the mean absolute percentage errors were lower for LSTM during peak periods, while SVR performed slightly better during off-peak periods. Additionally, LSTM coped substantially better than SVR with unusual traffic fluctuations. The sensitivity analysis of the missing input data in this study also revealed that the LSTM model was more robust to the high degree of missing data than the SVR model.

ABAFT: an adaptive weight-based fusion technique for travel time estimation using multi-source data with different confidence and spatial coverage

The evolution of traffic monitoring systems provides rich traffic data from multiple sensors. Fuzing the data has the potential to enhance the quality of travel time estimation. It also provides better spatial-temporal coverage in traffic observations. However, each sensor’s unique data collection process results in fusion challenges with respect to the coverage and data quality differences between various sources. These factors determine the degree of confidence that should be considered when fuzing different types of data. To this end, this paper proposes an adaptive weight-based fusion technique (ABAFT) that considers data spatial coverage and quality or confidence as the factors constructing the weight. The proposed ABAFT was tested using different scenarios on synthetic GPS and Bluetooth MAC Scanners data from an urban arterial corridor. The results show that the ABAFT can increase the travel time estimation accuracy by over 10%, and reliability by over 8% compared to the single sensor estimators. It also outperforms the simple average and standard-error-based fusion by around 4%. ABAFT is easy to be implemented on multiple sources of information available to transport agencies for a single point of truth.

Inland Vessel Travel Time Prediction via a Context-Aware Deep Learning Model

Accurate vessel travel time estimation is crucial for optimizing port operations and ensuring port safety. Existing vessel travel time prediction models primarily rely on path-finding algorithms and corresponding distance/speed relationships to calculate travel time. However, these models overlook the complex nature of vessel travel time, which is influenced by multiple traffic-related factors such as collision avoidance, shortest path selection, and vessel personnel performance. The lack of consideration for these specific aspects limits the accuracy and applicability of current models. We propose a novel context-aware deep learning approach for inland vessel travel time prediction. Firstly, we introduce a complex network that captures vessel–vessel interaction contexts, providing valuable traffic environment information as an input for the deep learning model. Additionally, we employ a convolutional neural network to extract spatial trajectory information, which is then integrated with interaction contexts and indirect context information. In the vessel travel time prediction procedure, we utilize a long short-term memory network to capture the temporal dependence within consecutive channel sections’ fused multiple context feature sets. Extensive experiments incorporating historical data from the Wuhan section of the Yangtze River in China demonstrate the superiority of our proposed model over classical models in predicting vessel travel time. Importantly, our model accounts for the specific traffic contexts that had previously been overlooked, leading to improved accuracy and applicability in inland vessel travel time prediction.

A Link Real-time Travel Time Estimation on Speed-time Field Traversing Incorporating LSTM Neural Network

A travel time estimation method based on Speed-time field traversal including LSTM neural network was proposed to increase the real-time and accuracy of travel time estimation. The node departure speed of the traditional piecewise truncated quadratic speed trajectory model was optimized by the road node arrival speed, considering the impact of road conditions on travel time. The node arrival speed was modeled as time series and predicted in the short-term future by combining the LSTM neural network model to construct a spatiotemporally continuous speed trajectory, to estimate the link’s travel time. The method was tested on an actual road and gave considerably improved estimates and results compared to the original method, using LSTM neural network to predict node departure speed and the technique using node arrival speed. According to experimental results, the proposed method performs well in both smooth and crowded traffic circumstances, serving as a benchmark for precise real-time travel time estimation on urban roads.

Fine-Grained Trajectory-Based Travel Time Estimation for Multi-City Scenarios Based on Deep Meta-Learning

Travel Time Estimation (TTE) is indispensable in intelligent transportation system (ITS). It is significant to achieve the fine-grained Trajectory-based Travel Time Estimation (TTTE) for multi-city scenarios, namely to accurately estimate travel time of the given trajectory for multiple city scenarios. However, it faces great challenges due to complex factors including dynamic temporal dependencies and fine-grained spatial dependencies. To tackle these challenges, we propose a meta learning based framework, MetaTTE, to continuously provide accurate travel time estimation over time by leveraging well-designed deep neural network model called DED, which consists of Data preprocessing module and Encoder-Decoder network module. By introducing meta learning techniques, the generalization ability of MetaTTE is enhanced using small amount of examples, which opens up new opportunities to increase the potential of achieving consistent performance on TTTE when traffic conditions and road networks change over time in the future. The DED model adopts an encoder-decoder network to capture fine-grained spatial and temporal representations. Extensive experiments on two real-world datasets are conducted to confirm that our MetaTTE outperforms six state-of-art baselines, and improve 29.35% and 25.93% accuracy than the best baseline on Chengdu and Porto datasets, respectively.

Multi-Semantic Path Representation Learning for Travel Time Estimation

Travel time estimation of a given path is a crucial task of Intelligent Transportation Systems (ITS). Accurate travel time estimation can benefit multiple downstream applications such as route planning, real-time navigation, and urban construction. However, it is a challenging problem since the travel time is largely affected by multiple complicated factors including spatial factors, temporal factors and external factors, and obtaining informative representations of a given path is not trivial. Most previous works solved this problem in either Euclidean space or non-Euclidean space, which was unilateral to represent the actual traveling path and led to relatively poor performance. To address this, this paper proposes a multi-semantic path representation method to exploit information in Euclidean space and non-Euclidean space simultaneously. First, since the path is composed of several segments, we generate semantic representations of segments in non-Euclidean space by taking both the time information and the historical co-occurrence into consideration. Second, as the path could be equally represented as several travelled intersections, semantic representations of intersection sequences are also extracted to improve the capability of the method by considering information in Euclidean space. Meanwhile, semantic representations from properties, including the length and the type of segments, are also incorporated into the model. Finally, a sequence learning component is added on the top to aggregate the information along the entire path and provides the final estimation. Extensive experiments were conducted on two real-world taxi trajectories datasets, and the experimental results demonstrate the superiority of the proposed method.

Accurate determination of the first arrival time of elastic wave traveled through rock sample by machine learning

To automate time-consuming and experience-based analysis of elastic wave velocities in rock samples, we propose a method of determining the arrival time of experimental waveform data using machine learning. Our model is one-dimensional semantic segmentation based on U-net utilizing ResNet and attention mechanisms. The results demonstrate high accuracy of travel time estimation (0.0125 μs) of two types of waves (P- and S-waves). Our approach can estimate the travel time in any experimental device. This method reduces the interpretation time and human biases in analyzing laboratory data of elastic waves. Therefore, this approach contributes to the efficient evaluation of subsurface structure from seismic properties.

MCT-TTE: Travel Time Estimation Based on Transformer and Convolution Neural Networks

In this paper, we propose a new travel time estimation framework based on transformer and convolution neural networks (CNN) to improve the accuracy of travel time estimation. We design a traffic information fusion component, which fuses the GPS trajectory, real road network, and external attributes, to fully consider the influence of road network topological characteristics as well as the traffic temporal characteristics on travel time estimation. Moreover, we provide a multiview CNN transformer component to capture the spatial information of each trajectory point at multiple regional scales. Extensive experiments on Chengdu and Beijing datasets show that the mean absolute percent error (MAPE) of our MCT-TTE is 11.25% and 11.78%, which is competitive with the state-of-the-arts baselines.

PR-LTTE: Link travel time estimation based on path recovery from large-scale incomplete trip data

The widespread use of positioning devices has generated large-scale trip data, boosting the study of traffic modeling. For instance, New York City Taxi & Limousine Commission regularly releases over 165 million taxi trip records containing the end-point information of each trip every year. Such big datasets provide us potential new perspectives to tackle traditional issues in traffic modeling. In this paper, we propose to use such vehicle trip data, which do not contain information on the intermediate vehicle positions other than the end points, to study the link (road segment) travel time estimation problem. A method named PR-LTTE is developed to estimate the mean travel time for interested links in a road network based on path recovery, jointly modeling trip distance and trip travel time. The key idea is to iteratively alternate between 1) inferring the most likely path for a given trip by minimizing a loss function that considers both trip distance and trip travel time, and 2) computing the link travel time via the least-squares estimation with the inferred path. Our experiments on two large-scale trip datasets (including New York City taxi trip data and Chengdu DiDi trip data) show very promising results. PR-LTTE improves the accuracy of travel time estimation by more than 50% in most cases over state-of-the-art methods.

An Adaptive Neural Fuzzy Inference System model for freeway travel time estimation based on existing detector facilities

While volume-counting detectors are currently installed in many freeways and have the capabilities of collecting high-resolution traffic information, travel time estimations are usually only based on spot speeds collected by the detectors, which could only produce rough estimates of travel time during peak hours when roadways are congested. In addition to spot speeds, these detectors provide directional volume counts and occupancy, which are useful for a better travel time estimation. This research used data from volume counting detectors and made Adaptive Neural Fuzzy Inference System (ANFIS) to automatically estimate real-time travel times. ANFIS’s ability to learn traffic patterns allows accurate and reliable real-time travel time estimation even with missing or corrupted data. ANFIS also proves to be a powerful tool for estimating future travel times on freeways. For easier use by practitioners and researchers, the method used in this study was implemented into a software package, named FTTE (Freeway Travel Time Estimator). Finally, a case study was conducted using the new approach and the results were compared for both congested and uncongested traffic conditions.

Spatiotemporal Features—Extracted Travel Time Prediction Leveraging Deep-Learning-Enabled Graph Convolutional Neural Network Model

Travel time prediction is one of the most important parameters to forecast network-wide traffic conditions. Travelers can access traffic roadway networks and arrive in their destinations at the lowest costs guided by accurate travel time estimation on alternative routes. In this study, we propose a long short-term memory (LSTM)-based deep learning model, deep learning on spatiotemporal features with Convolution Neural Network (DLSF-CNN), to extract the spatial–temporal correlation of travel time on different routes to accurately predict route travel time. Specifically, this model utilizes network-wide travel time, considering its topological structure as inputs, and combines convolutional neural network and LSTM techniques to accurately predict travel time. In addition to their spatial dependence, both coarse-grained and fine-grained temporal dependences are fully considered among the road segments along a route as well. The shift problem is formulated in the coarse-grained granularity to predict the route travel time in the next time interval. The experimental tests were conducted using real route travel time obtained by taxi trajectories in Harbin. The test results show that the travel time prediction accuracy of DLSF-CNN is above 90%. Meanwhile, the proposed model outperformed the other machine learning models based on multiple evaluation criteria. The RMSE (Root Mean Squard Error) and R2 (R Squared) increased by 18.6% and 22.46%, respectively. The results indicate the proposed model performs reasonably well under prevailing traffic conditions.

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.

Reliable Estimation of Urban Link Travel Time Using Multi-Sensor Data Fusion

Travel time is one of the most critical indexes to describe urban traffic operating states. How to obtain accurate and robust travel time estimates, so as to facilitate to make traffic control decision-making for administrators and trip-planning for travelers, is an urgent issue of wide concern. This paper proposes a reliable estimation method of urban link travel time using multi-sensor data fusion. Utilizing the characteristic analysis of each individual traffic sensor data, we first extract link travel time from license plate recognition data, geomagnetic detector data and floating car data, respectively, and find that their distribution patterns are similar and follow logarithmic normal distribution. Then, a support degree algorithm based on similarity function and a credibility algorithm based on membership function are developed, aiming to overcome the conflicts among multi-sensor traffic data and the uncertainties of single-sensor traffic data. The reliable fusion weights for each type of traffic sensor data are further determined by integrating the corresponding support degree with credibility. A case study was conducted using real-world data from a link of Jingshi Road in Jinan, China and demonstrated that the proposed method can effectively improve the accuracy and reliability of link travel time estimations in urban road systems.

Markov Chains based route travel time estimation considering link spatio-temporal correlation

Travel time is a critical measure for road network traffic conditions, and travel time estimation provides available information for travellers and traffic management. This paper proposes an improved method based on Markov Chains to estimate route travel time by considering spatio-temporal correlation from related links. The method mainly contains three parts. Firstly, in the light of traffic flow data collected from microwave detectors, Gaussian mixture model (GMM) is applied to cluster travel time data under two consecutive links, and thus capture the underlying traffic states. The transition probability matrix is constructed to estimate variations of traffic states over time. Then, link travel time distributions can be estimated from historical observations. Accordingly, we can estimate route travel time distribution by aggregating weighted link travel time distribution based on convolution theory. Finally, a case study including three experiments are used to test the accuracy of travel time estimation, we also compare the estimation performance of proposed model with several traditional methods, and the results indicate that the proposed model is effective and superior to traditional modes based on two indicators: Kullback–Leibler (KL) divergence and Mean Absolute Error (MAE).

Efficient Data Collection and Accurate Travel Time Estimation in a Connected Vehicle Environment Via Real-Time Compressive Sensing

Connected vehicles (CVs) can capture and transmit detailed data such as vehicle position and speed through vehicle-to-vehicle and vehicle-to-infrastructure communications. The wealth of CV data provides new opportunities to improve safety and mobility of transportation systems, which can overburden storage and communication systems. To mitigate this issue, we propose a compressive sensing (CS) approach that allows CVs to capture and compress data in real-time and later recover the original data accurately and efficiently. We evaluate our approach using two case studies. In the first study, we use our approach to recapture 10 million CV basic safety message (BSM) speed samples as well as other BSM variables. The results show that we can recover the original speed data with root-mean-squared error as low as 0.05 MPH. In the second study, a freeway traffic simulation model is built to evaluate the impact of our approach on travel time estimation. Multiple scenarios with various CV market penetration rates, On-board unit (OBU) capacities, compression ratios, arrival rate patterns, and data capture rates are simulated for our experiments. As a result, our approach provides more accurate estimation than conventional data collection methods by achieving up to 65% relative reduction in travel time estimation error. With a low compression ratio, our approach can still provide accurate estimation, therefore reducing OBU hardware costs. Lastly, our approach can improve travel time estimation accuracy when CVs are in traffic congestion as it provides a broader spatial–temporal coverage of traffic conditions and can accurately and efficiently recover the original CV data.

Efficient Collection of Connected Vehicles Data With Precision Guarantees

Connected vehicles disseminate detailed data, including their position and speed, at a very high frequency. Such data can be used for accurate real-time analysis, prediction and control of transportation systems. The outstanding challenge for such analysis is how to continuously collect and process extremely large volumes of data. To address this challenge, efficient collection of data is critical to prevent overburdening the communication systems and overreaching computational and memory capacity. In this work, we propose an efficient data collection scheme that selects and transmits only a small subset of the data to alleviate data transmission burden. As a demonstration, we have used the proposed approach to select the data points to be transmitted from 10,000 connected vehicles trips available in the Safety Pilot Model Deployment dataset. The presented results show that collection ratio can be as small as 0.05 depending on the required precision. Moreover, a simulation study was performed to evaluate the travel time estimation accuracy using the proposed data collection approach. Results show that the proposed data collection approach can significantly improve travel time estimation accuracy.

Travel time observation using numerical simulation of bender element testing in time and frequency domain

Due to poor understanding of the output signal, the reliability of the travel time estimated by bender element tests has been their most serious problem since their introduction to the soil testing. Recent establishment of a concept interpreting the bender element apparatus as a linear system enables us to calculate and probe the output signals more theoretically. In this study, first, the concept is reviewed to understand the linearity of the whole system, which is composed of some fundamental subsystems, reflecting the responses of the soil specimen, and the source/receiver bender elements including their peripherals. For modeling the response of the whole system, simplified models for the subsystems are employed and combined, with some updates to describe the interaction among the subsystems, by sharing some parameters throughout the models. With a set of hypothetical, but expected parameter values, the responses of each subsystem and the combined whole system are simulated in the time and frequency domains, and the resulting travel times are thoroughly examined. The validity of this model is demonstrated by comparing the system responses between the simulation and the experimental data previously conducted in the author’s laboratory. A potential that removing the response of the bender element subsystems from the whole response could dramatically improve the accuracy of travel time estimation in actual tests is concluded.

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%.

SharedEdge

Estimating travel time on the highway in real time is of great importance for transportation services. Previous work has been mainly focusing on the city scale for a particular transportation system, e.g., taxi, bus, and metro. Little research has been conducted to estimate fine-grained real-time travel time in state-level highway systems. This is because the traditional solutions based on probe vehicles or loop sensors cannot scale to state-level highway systems due to their large spatial coverage. Recently, the adoption of Electric Toll Collection (ETC) systems (e.g. EZ-pass) brings a new opportunity to estimate the real-time travel time in the highway systems with little marginal cost. However, the key challenge is that ETC data only record the coarse-grained total travel time between a pair of toll stations rather than fine-grained travel time in each individual highway edge. To address this challenge, we design SharedEdge to estimate the fine-grained edge travel time with large-scale streaming ETC data. The key novelty is that we estimate real-time fine-grained travel time (i.e., edge travel time) without using fine-grained data (i.e. GPS trajectories or loop sensor data), by a few techniques based on Bayesian Graphical models and Expectation Maximization. More importantly, we implement our SharedEdge in the Guangdong Province, China with an ETC system covering 69 highways and 773 toll stations with a length of 7, 000 km. Based on this implementation, we evaluate SharedEdge in details by comparing it with some baselines and the state-of-the-art models. The evaluation results show that SharedEdge outperforms other methods in terms of travel time estimation accuracy when compared with the ground truth obtained by 114 thousand GPS-equipped vehicles.

Urban link travel time estimation using traffic states‐based data fusion

Estimated travel time is a key input for many intelligent transport systems (ITS) applications and traffic management functions. There are numerous studies that show that fusing data from different sources such as global positioning system (GPS), Bluetooth, mobile phone network (MPN), and inductive loop detector (ILD) can result in more accurate travel time estimation. However, to date, there has been little research investigating the contribution of individual data sources to the quality of the final estimate or how this varies according to source-specific data quality under different traffic states. Here, three different data sources, namely bus-based GPS (bGPS) data, ILD data, and MPN data, of varying quality are combined using three different data fusion techniques of varying complexity. In order to quantify the accuracy of travel time estimation, travel time calculated using automatic number plate recognition (ANPR) data are used as the `ground truth'. The final results indicate that fusing multiple data together does not necessarily enhance the accuracy of travel time estimation. The results also show that even in dense urban areas, bGPS data, when combined with ILD data, can provide reasonable travel time estimates of general traffic stream under different traffic states.