Christopher Flores

A Wireless Accelerometer-Based Automatic Vehicle Classification Prototype System

Automatic vehicle classification (AVC) systems provide data about vehicle classes that are used for many purposes. This paper describes a prototype axle count and spacing AVC system using wireless accelerometers and magnetometers. The accelerometers detect vehicle axles, and the magnetometers report vehicle arrivals and departures and estimate speed. The prototype system is installed on Interstate 80 at Pinole, CA, USA, and tested under various traffic conditions. Video images and reports from a nearby commercial weigh-in-motion station provide ground truth to evaluate the performance of the system, including classification, axle spacing, and vehicle counts. The results show that the prototype AVC system is reliable in classifying vehicles even under congested traffic with accuracy of 99%.

Evaluation of Efficient Modes of Operation of GSM/GPRS Modules for M2M Communications

The field of machine-to-machine (M2M) communications has gained wide popularity and is steadily growing. This paper studies the feasibility of using the Global System for Mobile Communications and in particular the General Packet Radio Service (GPRS) for a low data rate long- lasting battery-powered operation of M2M devices. A model is introduced to estimate the power consumption of a GPRS connection. It allows the identification and evaluation of optimizations of the data transmission procedures. Two M2M modes of GPRS operation are introduced. For applications with frequent transmissions, an Always- on-mode turns out to be most reasonable. For infrequent transmissions, e.g., one transmission every 2 hours, an On/off-mode reduces the power consumption of M2M devices by 93% as compared to the Always-on-mode. With a 3-cell battery providing 25.9 Wh of energy and considering only the power consumption of the communication module, a battery lifetime of up to 5 years is feasible. Measurements show that usually 40% of the energy spent for a short data transmission is wasted by one particular GPRS procedure called non-DRX period. Avoiding this saves up to 35% of total average power, depending on the rate of data transmissions.

In-pavement wireless weigh-in-motion

Truck weight data is used in many areas of transportation such as weight enforcement and pavement condition assessment. This paper describes a wireless sensor network (WSN) that estimates the weight of moving vehicles from pavement vibrations caused by vehicular motion. The WSN consists of: acceleration sensors that report pavement vibration; vehicle detection sensors that report a vehicles arrival and departure times; and an access point (AP) that synchronizes all the sensors and records the sensor data. The paper also describes a novel algorithm that estimates a vehicles weight from pavement vibration and vehicle detection data, and calculates pavement deflection in the process. A prototype of the system has been deployed near a conventional Weigh-In-Motion (WIM) system on I-80 W in Pinole, CA. Weights of 52 trucks at different speeds and loads were estimated by the system under different pavement temperatures and varying environmental conditions, adding to the challenges the system must overcome. The error in load estimates was less than 10% for gross weight and 15% for individual axle weights. Different states have different requirements for WIM but the system described here outperformed the nearby conventional WIM, and meets commonly used standards in United States. The system also opens up exciting new opportunities for WSNs in pavement engineering and intelligent transportation.

Vehicle re-identification using wireless magnetic sensors: Algorithm revision, modifications and performance analysis

A vehicle re-identification method based on matching vehicle signatures obtained from wireless magnetic sensors was studied on a single lane loop on-ramp. Different modifications were implemented in the algorithm in order to address limitations of the system when vehicles stop/move slowly over the detectors. The original and modified vehicle re-identification algorithm results were compared against ground truth data obtained from video. Based on the ground truth data it was possible to determine the percentage of vehicles that are re-identified and the number of those vehicles that are misidentified as a function of different algorithm parameters. For this analysis, vehicles were divided into two subsets: i) uncongested and ii) congested. The original method mismatched percentage was around or below 15% for the uncongested vehicle subset and between 20% to 60% for the congested one. With the modified method it was possible to improve the matching rate as well as the accuracy of the matching algorithm. For the uncongested subset, the modified method showed a higher vehicle re-identification rate while maintaining the mismatched percentage around or below 8%. The main improvement over the original method was achieved on the congested vehicle subset, since the number of re-identified vehicles was increased over the original method while keeping the mismatched percentage around or below 14%.

Arterial travel time estimation based on vehicle re-identification using magnetic sensors: Performance analysis

Two versions of an arterial travel time estimation method based on vehicle re-identification using wireless magnetic sensors were studied across an arterial segment with multiple intersections. Both methods are based on the same travel time estimation system, but one of them uses the so called original signal processing algorithm while the other one uses a recently modified version of it. Both methods were tested on a 0.51 km (0.32 mile)-long segment of West 34th Street in New York, NY, under harsh driving conditions (i.e. right after a winter storm). The original and modified system results were compared against ground truth data obtained from video. Based on the ground truth data it was possible to determine the travel time distribution and the percentage of vehicles that each of the different methods was able to re-identify. During an analysis period of 45 minutes, 318 vehicles were registered to go across the arterial segment. The original method has a 62% re-identification rate, while the modified method has a 69% rate. Based on comparisons of travel time distribution and empirical cumulative distribution functions, it was observed that the modified method travel time distribution is closely related to the ground truth distribution, while the original method significantly diverges from the ground truth at long travel times.

High-resolution Sensing of Urban Traffic

Cities do not collect the high-resolution (HR) traffic data needed to evaluate and improve roadway operation. This paper describes a HR system called SAMS (Safety and Mobility System) that detects and records the lane, speed, signal phase and time when each vehicle enters and leaves the intersection; fuses these sensor events to estimate the intersection traffic state in real time for use by signal controllers; and archives the time series of traffic states to produce reports of vehicle counts and turn ratios, saturation rates, queues, waiting times, Purdue Coordination Diagram, and level of service (LOS); red light, speed, and right-turn-on-red (RTOR) violations, and vehicle-vehicle conflicts. The reports can be used to evaluate the performance of the current road operation and to improve traffic control.

Parking Sensing and Information System: Sensors, Deployment, and Evaluation

This paper describes a smart parking sensing and information system that disseminates parking availability information to public users in a cost-effective and efficient manner. The hardware framework of the system is built on advanced wireless sensor networks and cloud service over the Internet, and the system is highly scalable. The parking information provided to the users is set in the form of occupancy rates and expected cruising time. Both are obtained from an analytical algorithm that processes historical and real-time data and are then visualized in a color theme. The entire parking system is deployed and extensively evaluated at Stanford University, California, Parking Structure 1.

Development of a Cost‐Effective Wireless Vibration Weigh‐In‐Motion System to Estimate Axle Weights of Trucks

Truck weight data plays an important role in weight enforcement and pavement condition assessment. This data is primarily obtained through weigh stations and Weigh-In-Motion (WIM) stations which are currently very expensive to install and maintain. This article presents results of the implementation of an inexpensive wireless sensor-based vibration WIM system. The proposed wireless sensor network (WSN) consists of acceleration sensors that report pavement vibration; vehicle detection sensors that report a vehicles arrival and departure times; and an access point (AP) that synchronizes all the sensors and records the sensor data. The article also describes a new method for speed compensation, an energy-efficient algorithm (adaptive sampling method) to increase battery life, and a new modeling procedure to estimate gross vehicle weights. The system deployed near a conventional WIM system on I-80W in Pinole, CA passed the accuracy standards for WIM systems and outperformed a nearby commercial WIM station, based on conventional technology.