Adopting and incorporating crowdsourced traffic data in advanced transportation management systems

The widespread availability of internet and mobile devices has made crowdsourced reports a considerable source of information in many domains. Traffic managers, among others, have started using crowdsourced traffic incident reports (CSTIRs) to complement their existing sources of traffic monitoring. One of the prominent providers of CSTIRs is Waze. In this dissertation, first a quantitative analysis was conducted to evaluate Waze data in comparison to the existing sources of Iowa Department of Transportation. The potential added coverage that Waze can provide was also estimated. Redundant CSTIRs of the same incident were found to be one of the main challenges of Waze and CSTIRs in general. To leverage the value of the redundant reports and address this challenge, a state-of-the-art cluster analysis was implemented to reduce the redundancies, while providing further information about the incident. The clustered CSTIRs indicate the area impacted by an incident and provide a basis for estimating the reliability of the cluster. Furthermore, the challenges with clustering CSTIRs were described and recommendations were made for parameter tuning and cluster validation. Finally, an open-source software package was offered to implement the clustering method in near real-time. This software downloads and parses the raw data, implements clustering, tracks clusters, assigns a reliability score to clusters, and provides a RESTful API for information dissemination portals and web pages to use the data for multiple applications within the DOT and for the general public. With emerging technologies such as connected vehicles and vehicle-to-infrastructure (V2I) communication, CSTIRs and similar type of data are expected to grow. The findings and recommendations in this work, although implemented on Waze data, will be beneficial to the analysis of these emerging sources of data

Big Data Mining to Construct Truck Tours

Cross-Border shipping of goods among different distributors is an essential part of transportation across Canada and U.S. These two countries are heavily dependent on border crossing locations to facilitate international trade between each other. This research considers the identification of the international tours accomplishing the shipping of goods. A truck tour is a round trip where a truck starts its journey from its firm or an industry, performing stops for different purposes that include taking a rest, fuel refilling, and transferring goods to multiple locations, and returns back to its initial firm location. In this thesis, we present a three step method on mining GPS truck data to identify all possible truck tours belonging to different carriers. In the first step, a clustering technique is applied on the stop locations to discover the firm for each carrier. A modified DBSCAN algorithm is proposed to achieve this task by automatically determining the two input parameters based on the data points provided. Various statistical measures like count of unique trucks and count of truck visits are applied on the resulting clusters to identify the firms of the respective carriers. In the second step, we tackle the problem of classifying the stop locations into two types: primary stops, where goods are transferred, and secondary stops like rest stations, where vehicle and driver needs are met. This problem is solved using one of the trade indicator called Specialization Index. Moreover, several set of features are explored to build the classification model to classify the type of stop locations. In the third step, having identified the firm, primary and secondary locations, an automated path finder is developed to identify the truck tours starting from each firm. The results of the specialization index and the feature-based classification in identifying stop events are compared with the entropy index from previous work. Experimental results show that the proposed set of cluster features significantly add classification power to our model giving 98.79% accuracy which in turn helps in discovering accurate tours