Shin-Ting Jeng

Real-Time Vehicle Classification Using Inductive Loop Signature Data

Vehicle class is an important characteristic of traffic measurement, and classification information can contribute to many important applications in various transportation fields. For instance, vehicle classification is helpful in monitoring heavy vehicle traffic for road maintenance and safety, modeling traffic flow, and obtaining performance measurements based on each vehicle class for traffic surveillance. A real-time vehicle classification model was introduced. A heuristic method combined with decision tree and K-means clustering approaches was proposed to develop the vehicle classification model. The features used in the proposed model were extracted from piecewise slope rate values, which were obtained from single-loop inductive signature data. Three vehicle classification schemes–FHWA, FHWA-I, and Real-time Traffic Performance Measurement System–and a data set obtained from square single-loop detectors were used for model development. A data set obtained from round single-loop detectors was applied to test the transferability of the proposed model. The results demonstrated that the proposed real-time vehicle classification model is not only capable of categorizing vehicle types on the basis of the FHWA scheme but also is capable of grouping vehicles into more detailed classes. The classification model can successfully classify vehicles into 15 classes using single-loop detector data without any explicit axle information. In addition, the advantages of the proposed vehicle classification model are its simplicity, its use of the current detection infrastructure, and its enhancement of the use of single-loop detectors for vehicle classification. The initial results also suggest the potential for transferability of the vehicle classification approach and are very encouraging.

Anonymous Vehicle Reidentification Using Heterogeneous Detection Systems

An innovative feature of this paper is the demonstration of the feasibility of real-time vehicle reidentification algorithm development at a signalized intersection, where different traffic detection technologies were employed at upstream and downstream locations. Previous research by the authors on vehicle reidentification has utilized the same traffic sensors (e.g., conventional square inductive loops) and detectors (e.g., high-speed scanning detector cards) at both locations. In this paper, an opportunity arose for the first time to collect a downstream data set from a temporary installation of a prototype innovative inductive loop sensor known as a ldquobladerdquo sensor in conjunction with conventional inductive loops upstream. At both locations, advanced high-speed scanning detector cards were used. Although the number of vehicles for which data could be collected was small, encouraging results were obtained for vehicle reidentification performance in this system of mixed traffic detection technologies. In future large-scale applications of vehicle reidentification approaches for real-time traffic performance measurement, management, and control, it would be most beneficial and practical if heterogeneous and homogeneous detection systems could be supported. This initial paper yielded many useful insights about this important issue and demonstrated on a small scale the feasibility of vehicle reidentification in a system with heterogeneous detection technologies.

Freeway Corridor Performance Measurement Based on Vehicle Reidentification

Section-related or link-based traffic sensor data can provide reliable and accurate inputs for traffic-performance-measurement systems. Section performance measurements can easily be generated via a vehicle-reidentification system. Inductive loop-detector (ILD)-based systems are cost effective because ILDs are widely installed in the field (with fewer market penetration concerns) and provide essentially anonymous surveillance with few, if any, privacy concerns. Accordingly, the authors have recently developed an algorithm, i.e., RTREID-2, using inductive loop signature-based methods for vehicle reidentification (ILD-VReID) and which was dedicated to meet the needs for real-time implementation and section-performance measurement. RTREID-2 was developed by utilizing a piecewise slope rate (PSR) approach to transform the raw vehicle signatures obtained from square loops (only). This paper reports the results of a 10.0-km (6.2-mi) freeway corridor implementation of RTREID-2 under congested morning peak-period conditions. Although RTREID-2 has been designed for real-time operation, this initial corridor investigation was conducted offline. The corridor contained mostly round inductive loop sensors with some square loops, providing an opportunity to assess the applicability and transferability of RTREID-2 to homogenous and heterogeneous loop-sensor systems. Analyses of travel time and speed at both freeway corridor and individual freeway section levels were conducted, and excellent results were obtained compared with Global Positioning System (GPS) measurements from control vehicles. The results suggest that RTREID-2 has the potential to successfully be implemented in a congested freeway corridor, utilizing either or both round or square inductive loop sensors.

Design and Initial Implementation of an Inductive Signature-Based Real-Time Traffic Performance Measurement System

The need for accurate, comprehensive and timely traffic surveillance information is critical to ensure optimal traffic operations and management for advanced traffic management systems (ATMS). This paper describes an on-going study that involves the design and implementation of the section-based freeway real-time traffic performance measurement system (RTPMS)-an advanced surveillance system based on inductive vehicle signature technologies. Unlike traffic performance measurement systems that depend on point measures, RTPMS provides section-based travel time measures via matching of inductive vehicle signatures obtained at two adjacent detector station locations. Hence, the performance measures account for traffic conditions spanning an entire section, not just at a local detector station. In addition, each re-identified vehicle is also classified in RTPMS, yielding detailed section-based performance measures of different vehicle classes. This gives the ability to obtain more accurate travel statistics and vehicle exposure rates, such as those of commercial vehicles.

A New Approach to Estimate Vehicle Emissions Using Inductive Loop Detector Data

Motor vehicles are significant contributors to urban air pollution and greenhouse gases. Common practice for estimating vehicle emissions in California calls for integrating travel forecasting models and emission models. However, static travel forecasting models are incapable of generating the detailed vehicle activity required for emission estimates. Further, the fleet mix is also assumed to be constant across different roadways and at all times of day. Therefore, this article attempts to develop a new approach to measure travel activity and vehicle mix using existing inductive loop detector data. However, this study does not intend to forecast future vehicle activity. The study found that current practices overestimate speeds as much as 5–25 mph, whereas the proposed method overestimates speed about 2 mph, compared to ground-truth speeds in a freeway corridor. Furthermore, contrary to current practice, the proposed model distinguishes the vehicle miles traveled (VMT) between light-duty vehicles and heavy-duty vehicles in each link. The current practice overestimates or underestimates emissions by 1–20% during different times of day, whereas the proposed method underestimates the emissions by about 3%. We conclude that the proposed approach can provide a cost-effective way of estimating reliable emission inventory and estimating time-dependent emission inventories for different pollutants.