Dynamic Travel Time Estimation for Northeast Illinois Expressways

Having access to accurate travel time is critical for both highway network users and traffic operators. Travel time that is currently reported for most highways is estimated by employing naïve methods that use limited sources of data. This might result in inaccurate travel time prediction and could impose difficulties on travelers. The purpose of this report is to develop an enhanced travel time prediction model using multiple data sources, including loop detectors, probe vehicles, weather condition, geometry, roadway incidents, roadwork, special events, and sun glare. Different models are trained accordingly based on machine learning techniques to predict travel time 5 min, 10 min, and even 60 min ahead. A comparison of techniques showed that 15 min or shorter prediction horizons are more accurate when applying the random forest model, although the prediction accuracy of longer prediction horizons is still acceptable. An algorithm is proposed for dynamic prediction of travel time in which the travel time of each highway corridor is calculated by adding the predicted travel time of each link of the corridor. The proposed dynamic approach is tested and evaluated on highways and showed a significant improvement in the accuracy of predicted travel time in comparison to the snapshot travel time prediction approach. Traffic-related variables, especially occupancy, are found to be effective in short-term travel time prediction using loop-detector data. This suggests that among traffic variables collected by loop detectors, occupancy can capture traffic condition better than other variables. Fusion of several data sources, however, increases prediction accuracy of the models.IDOT-R27-177Ope

Traffic Condition Assessment: Integrated Dynamic Travel Time Prediction and Accident Detection Models

Having access to accurate travel time is of great importance for both highway network users and traffic operators. The travel time which is currently reported for most highways is estimated by employing naïve methods and using limited sources of data. This might result in inaccurate travel time prediction and could impose difficulties on travelers. Therefore, proposing a comprehensive framework that utilizes various data sources and appropriate methodologies in order to predict the travel time accurately is essential. In this thesis, a hybrid dynamic approach is proposed to predict travel time of highway corridors under two traffic conditions: regular traffic condition and accident condition. To this end, first enhanced travel time prediction models for highway links are developed. The data, which is used in these models includes traffic spatiotemporal, geometry, weather condition, road work, special events, and accidents data. On the other hand, accident as an influential factor can considerably impact travel time. Therefore, advanced techniques including machine learning, deep learning, and deep ensemble models are developed to detect traffic accidents of highways in real time. Thereafter, a dynamic travel time prediction approach is suggested for highway corridors using integration of the developed travel time prediction and accident detection models of highway links. Finally, to find the optimal estimation of travel time for highway corridors, an ensemble based Kalman Filter algorithm is proposed that consequently led to boosting the prediction accuracy

Route Choice Estimation Using Cell Phone Data

Nowadays development of cell phone network provides huge and ubiquitous data, with wide application in transportation science. One of the most important advantages of these kinds of data is enabling the process of collecting information without any active users’ interference. A big data set consisting of 300,000 cell phone users’ information in Shiraz are studied. This data set includes spatiotemporal information of travelers for every 5 minutes in a time span of 40 hours in two consecutive days. The spatial part of each user’s information contains the position of the BTS (Base Transceiver Station) to which his cell phone is currently connected. Due to the existence of outliers, it is necessary to smooth the data initially. One of the main reasons of errors in the data set is ping pong handover, which leads to false transitions and must be eliminated. After the data preparation, stay locations are determined for each user and a trajectory for each pair of origin and destination is estimated. At this step based on network information of the city, a method to match trajectories with the network map is applied. Finally the obtained results indicate whether travelers choose the shortest path or other possible alternatives

Route Choice Estimation Using Cell Phone Data

Nowadays development of cell phone network provides huge and ubiquitous data, with wide application in transportation science. One of the most important advantages of these kinds of data is enabling the process of collecting information without any active users’ interference. A big data set consisting of 300,000 cell phone users’ information in Shiraz are studied. This data set includes spatiotemporal information of travelers for every 5 minutes in a time span of 40 hours in two consecutive days. The spatial part of each user’s information contains the position of the BTS (Base Transceiver Station) to which his cell phone is currently connected. Due to the existence of outliers, it is necessary to smooth the data initially. One of the main reasons of errors in the data set is ping pong handover, which leads to false transitions and must be eliminated. After the data preparation, stay locations are determined for each user and a trajectory for each pair of origin and destination is estimated. At this step based on network information of the city, a method to match trajectories with the network map is applied. Finally the obtained results indicate whether travelers choose the shortest path or other possible alternatives

Application of Machine Learning Techniques in Short-term Travel Time Prediction Using Multiple Data Sources

Having access to accurate travel time is of great importance for both highway network users and traffic engineers. The travel time which is currently reported on several highways is estimated by employing naïve methods and using limited sources of data. This results in unreliable and inaccurate travel time prediction and could impose delay on travelers. Therefore, the main objective of this study is short-term prediction of travel time for highways using multiple data sources including loop detectors, probe vehicles, weather condition, network, accidents, road works, and special events in order to consider the effect of different factors on travel time. To this end, two machine learning methods, K-Nearest Neighbors and Random Forest, are employed. After applying data cleaning process on datasets and combining them, the models are trained to predict and compare short-term harmonic average speed as a representative of travel time for 5-minute prediction horizons in one hour ahead. The travel time is calculated as the ratio of the length of each link and the harmonic average speed for all reporting vehicles. Hence, a model is trained for each technique to predict travel time 5 minutes ahead, 10 minutes ahead, and all the way down to 60 minutes ahead. The results confirm satisfying performance of both models in short-term travel time prediction with slightly outperformance of Random Forest model. A feature importance and sensitivity analysis also applied for the Random Forest model, and traffic variables are found as the most effective variables in predicting the travel time.</p

Application of Machine Learning Techniques in Short-term Travel Time Prediction Using Multiple Data Sources

Having access to accurate travel time is of great importance for both highway network users and traffic engineers. The travel time which is currently reported on several highways is estimated by employing naïve methods and using limited sources of data. This results in unreliable and inaccurate travel time prediction and could impose delay on travelers. Therefore, the main objective of this study is short-term prediction of travel time for highways using multiple data sources including loop detectors, probe vehicles, weather condition, network, accidents, road works, and special events in order to consider the effect of different factors on travel time. To this end, two machine learning methods, K-Nearest Neighbors and Random Forest, are employed. After applying data cleaning process on datasets and combining them, the models are trained to predict and compare short-term harmonic average speed as a representative of travel time for 5-minute prediction horizons in one hour ahead. The travel time is calculated as the ratio of the length of each link and the harmonic average speed for all reporting vehicles. Hence, a model is trained for each technique to predict travel time 5 minutes ahead, 10 minutes ahead, and all the way down to 60 minutes ahead. The results confirm satisfying performance of both models in short-term travel time prediction with slightly outperformance of Random Forest model. A feature importance and sensitivity analysis also applied for the Random Forest model, and traffic variables are found as the most effective variables in predicting the travel time

Best Practice Operation of Reversible Express Lanes for the Kennedy Expressway

Reversible lanes in Chicago’s Kennedy Expressway are an available infrastructure that can significantly improve traffic performance; however, a special focus on congestion management is required to improve their operation. This research project aims to evaluate and improve the operation of reversible lanes in the Kennedy Expressway. The Kennedy Expressway is a nearly 18-mile-long freeway in Chicago, Illinois, that connects in the southeast to northwest direction between the West Loop and O’Hare International Airport. There are two approximately 8-mile reversible lanes in the Kennedy Expressway’s median, where I-94 merges into I-90, and there are three entrance gates in each direction of this corridor. The purpose of the reversible lanes is to help the congested direction of the Kennedy Expressway increase its traffic flow and decrease the delay in the whole corridor. Currently, experts in a control location switch the direction of the reversible lanes two to three times per day by observing real-time traffic conditions captured by a traffic surveillance camera. In general, inbound gates are opened and outbound gates are closed around midnight because morning traffic is usually heavier toward the central city neighborhoods. In contrast, evening peak-hour traffic is usually heavier toward the outbound direction, so the direction of the reversible lanes is switched from inbound to outbound around noon. This study evaluates the Kennedy Expressway’s current reversing operation. Different indices are generated for the corridor to measure the reversible lanes’ performance, and a data-driven approach is selected to find the best time to start the operation. Subsequently, real-time and offline instruction for the operation of the reversible lanes is provided through employing deep learning and statistical techniques. In addition, an offline timetable is also provided through an optimization technique. Eventually, integration of the data-driven and optimization techniques results in the best practice operation of the reversible lanes.IDOT-ICT-195Ope