Dynamic Arrival Rate Estimation for Campus Mobility on Demand Network Graphs

Mobility On Demand (MOD) systems are revolutionizing transportation in urban settings by improving vehicle utilization and reducing parking congestion. A key factor in the success of an MOD system is the ability to measure and respond to real-time customer arrival data. Real time traffic arrival rate data is traditionally difficult to obtain due to the need to install fixed sensors throughout the MOD network. This paper presents a framework for measuring pedestrian traffic arrival rates using sensors onboard the vehicles that make up the MOD fleet. A novel distributed fusion algorithm is presented which combines onboard LIDAR and camera sensor measurements to detect trajectories of pedestrians with a 90% detection hit rate with 1.5 false positives per minute. A novel moving observer method is introduced to estimate pedestrian arrival rates from pedestrian trajectories collected from mobile sensors. The moving observer method is evaluated in both simulation and hardware and is shown to achieve arrival rate estimates comparable to those that would be obtained with multiple stationary sensors.Comment: Appears in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). http://ieeexplore.ieee.org/abstract/document/7759357

Examining the potential of floating car data for dynamic traffic management

Traditional traffic monitoring systems are mostly based on road side equipment (RSE) measuring traffic conditions throughout the day. With more and more GPS-enabled connected devices, floating car data (FCD) has become an interesting source of traffic information, requiring only a fraction of the RSE infrastructure investment. While FCD is commonly used to derive historic travel times on individual roads and to evaluate other traffic data and algorithms, it could also be used in traffic management systems directly. However, as live systems only capture a small percentage of all traffic, its use in live operating systems needs to be examined. Here, the authors investigate the potential of FCD to be used as input data for live automated traffic management systems. The FCD in this study is collected by a live country-wide FCD system in the Netherlands covering 6-8% of all vehicles. The (anonymised) data is first compared to available road side measurements to show the current quality of FCD. It is then used in a dynamic speed management system and compared to the installed system on the studied highway. Results indicate the FCD set-up can approximate the installed system, showing the feasibility of a live system

DYNAMIC FREEWAY TRAVEL TIME PREDICTION USING SINGLE LOOP DETECTOR AND INCIDENT DATA

The accurate estimation of travel time is valuable for a variety of transportation applications such as freeway performance evaluation and real-time traveler information. Given the extensive availability of traffic data collected by intelligent transportation systems, a variety of travel time estimation methods have been developed. Despite limited success under light traffic conditions, traditional corridor travel time prediction methods have suffered various drawbacks. First, most of these methods are developed based on data generated by dual-loop detectors that contain average spot speeds. However, single-loop detectors (and other devices that emulate its operation) are the most commonly used devices in traffic monitoring systems. There has not been a reliable methodology for travel time prediction based on data generated by such devices due to the lack of speed measurements. Moreover, the majority of existing studies focus on travel time estimation. Secondly, the effect of traffic progression along the freeway has not been considered in the travel time prediction process. Moreover, the impact of incidents on travel time estimates has not been effectively accounted for in existing studies.The objective of this dissertation is to develop a methodology for dynamic travel time prediction based on continuous data generated by single-loop detectors (and similar devices) and incident reports generated by the traffic monitoring system. This method involves multiple-step-ahead prediction for flow rate and occupancy in real time. A seasonal autoregressive integrated moving average (SARIMA) model is developed with an embedded adaptive predictor. This predictor adjusts the prediction error based on traffic data that becomes available every five minutes at each station. The impact of incidents is evaluated based on estimates of incident duration and the queue incurred.Tests and comparative analyses show that this method is able to capture the real-time characteristics of the traffic and provide more accurate travel time estimates particularly when incidents occur. The sensitivities of the models to the variations of the flow and occupancy data are analyzed and future research has been identified.The potential of this methodology in dealing with less than perfect data sources has been demonstrated. This provides good opportunity for the wide application of the proposed method since single-loop type detectors are most extensively installed in various intelligent transportation system deployments

Short-term traffic predictions on large urban traffic networks: applications of network-based machine learning models and dynamic traffic assignment models

The paper discusses the issues to face in applications of short-term traffic predictions on urban road networks and the opportunities provided by explicit and implicit models. Different specifications of Bayesian Networks and Artificial Neural Networks are applied for prediction of road link speed and are tested on a large floating car data set. Moreover, two traffic assignment models of different complexity are applied on a sub-area of the road network of Rome and validated on the same floating car data set