Arash Jahangiri

Applying Machine Learning Techniques to Transportation Mode Recognition Using Mobile Phone Sensor Data

This paper adopts different supervised learning methods from the field of machine learning to develop multiclass classifiers that identify the transportation mode, including driving a car, riding a bicycle, riding a bus, walking, and running. Methods that were considered include K-nearest neighbor, support vector machines (SVMs), and tree-based models that comprise a single decision tree, bagging, and random forest (RF) methods. For training and validating purposes, data were obtained from smartphone sensors, including accelerometer, gyroscope, and rotation vector sensors. K-fold cross-validation as well as out-of-bag error was used for model selection and validation purposes. Several features were created from which a subset was identified through the minimum redundancy maximum relevance method. Data obtained from the smartphone sensors were found to provide important information to distinguish between transportation modes. The performance of different methods was evaluated and compared. The RF and SVM methods were found to produce the best performance. Furthermore, an effort was made to develop a new additional feature that entails creating a combination of other features by adopting a simulated annealing algorithm and a random forest method.

Red-light running violation prediction using observational and simulator data

In the United States, 683 people were killed and an estimated 133,000 were injured in crashes due to running red lights in 2012. To help prevent/mitigate crashes caused by running red lights, these violations need to be identified before they occur, so both the road users (i.e., drivers, pedestrians, etc.) in potential danger and the infrastructure can be notified and actions can be taken accordingly. Two different data sets were used to assess the feasibility of developing red-light running (RLR) violation prediction models: (1) observational data and (2) driver simulator data. Both data sets included common factors, such as time to intersection (TTI), distance to intersection (DTI), and velocity at the onset of the yellow indication. However, the observational data set provided additional factors that the simulator data set did not, and vice versa. The observational data included vehicle information (e.g., speed, acceleration, etc.) for several different time frames. For each vehicle approaching an intersection in the observational data set, required data were extracted from several time frames as the vehicle drew closer to the intersection. However, since the observational data were inherently anonymous, driver factors such as age and gender were unavailable in the observational data set. Conversely, the simulator data set contained age and gender. In addition, the simulator data included a secondary (non-driving) task factor and a treatment factor (i.e., incoming/outgoing calls while driving). The simulator data only included vehicle information for certain time frames (e.g., yellow onset); the data did not provide vehicle information for several different time frames while vehicles were approaching an intersection. In this study, the random forest (RF) machine-learning technique was adopted to develop RLR violation prediction models. Factor importance was obtained for different models and different data sets to show how differently the factors influence the performance of each model. A sensitivity analysis showed that the factor importance to identify RLR violations changed when data from different time frames were used to develop the prediction models. TTI, DTI, the required deceleration parameter (RDP), and velocity at the onset of a yellow indication were among the most important factors identified by both models constructed using observational data and simulator data. Furthermore, in addition to the factors obtained from a point in time (i.e., yellow onset), valuable information suitable for RLR violation prediction was obtained from defined monitoring periods. It was found that period lengths of 2-6m contributed to the best model performance.

Modeling and Assessment of Crossing Elimination for No-Notice Evacuations

Crossing elimination is a relatively recent strategy that emergency managers and departments of transportation may consider during no-notice evacuations. In this strategy, certain intersection movements that may be permissible under normal operating conditions are prohibited so that arterial traffic flow will increase. A few previous studies examined this strategy with contraflow operations. However, the benefits of crossing elimination alone remain unclear. An assessment of the effects of intersection crossing elimination during evacuations helps fill this knowledge gap. A simulation–optimization model was developed to determine the near-optimal configuration of intersection movements from a set of pre-specified possible configurations for intersections in a given area. At the optimization level, the total travel time of evacuees was minimized, and at the lower level, all traffic was assigned to the network with DYNUST traffic assignment simulation software. A simulated annealing heuristic was used to solve the optimization problem. The entire method was applied to a real network to assess the impact of crossing elimination. Three scenarios were developed with combinations of evacuee destination and departure time distributions. Results for these scenarios indicated that total evacuee travel time was improved by about 3% to 5% (9,700 to 11,200 h for about 300,000 evacuees). The availability of through movements and the elimination of merging points were the two factors influencing the selection of modified configurations for intersection movement.

Modeling driver stop/run behavior at the onset of a yellow indication considering driver run tendency and roadway surface conditions

The ability to model driver stop/run behavior at signalized intersections considering the roadway surface condition is critical in the design of advanced driver assistance systems. Such systems can reduce intersection crashes and fatalities by predicting driver stop/run behavior. The research presented in this paper uses data collected from two controlled field experiments on the Smart Road at the Virginia Tech Transportation Institute (VTTI) to model driver stop/run behavior at the onset of a yellow indication for different roadway surface conditions. The paper offers two contributions. First, it introduces a new predictor related to driver aggressiveness and demonstrates that this measure enhances the modeling of driver stop/run behavior. Second, it applies well-known artificial intelligence techniques including: adaptive boosting (AdaBoost), random forest, and support vector machine (SVM) algorithms as well as traditional logistic regression techniques on the data in order to develop a model that can be used by traffic signal controllers to predict driver stop/run decisions in a connected vehicle environment. The research demonstrates that by adding the proposed driver aggressiveness predictor to the model, there is a statistically significant increase in the model accuracy. Moreover the false alarm rate is significantly reduced but this reduction is not statistically significant. The study demonstrates that, for the subject data, the SVM machine learning algorithm performs the best in terms of optimum classification accuracy and false positive rates. However, the SVM model produces the best performance in terms of the classification accuracy only.

A Network-Wide Bilevel Optimization-Simulation Approach for Variable Speed Limit Systems to Improve Travel Time Reliability

AbstractTraffic congestion is a serious challenge that urban transportation systems are facing. Variable speed limit (VSL) systems are one of the countermeasures to reduce traffic congestion and sm...