scholarly journals Methodology for Calculating Latency of GPS Probe Data

2017 ◽  
Vol 2645 (1) ◽  
pp. 76-85 ◽  
Author(s):  
Zhongxiang Wang ◽  
Masoud Hamedi ◽  
Stanley Young
Keyword(s):  
Real Time ◽  
Ground Truth ◽  
Transportation Systems ◽  
Traffic Information ◽  
Reference Source ◽  
Traveler Information ◽  
Ground Truth Data ◽  
Real Time Traffic ◽  
Message Signs ◽  
Road Segmentation

Crowdsourced GPS probe data, such as travel time on changeable-message signs and incident detection, have been gaining popularity in recent years as a source for real-time traffic information to driver operations and transportation systems management and operations. Efforts have been made to evaluate the quality of such data from different perspectives. Although such crowdsourced data are already in widespread use in many states, particularly the high traffic areas on the Eastern seaboard, concerns about latency—the time between traffic being perturbed as a result of an incident and reflection of the disturbance in the outsourced data feed—have escalated in importance. Latency is critical for the accuracy of real-time operations, emergency response, and traveler information systems. This paper offers a methodology for measuring probe data latency regarding a selected reference source. Although Bluetooth reidentification data are used as the reference source, the methodology can be applied to any other ground truth data source of choice. The core of the methodology is an algorithm for maximum pattern matching that works with three fitness objectives. To test the methodology, sample field reference data were collected on multiple freeway segments for a 2-week period by using portable Bluetooth sensors as ground truth. Equivalent GPS probe data were obtained from a private vendor, and their latency was evaluated. Latency at different times of the day, impact of road segmentation scheme on latency, and sensitivity of the latency to both speed-slowdown and recovery-from-slowdown episodes are also discussed.

scholarly journals Cross-Vendor and Cross-State Analysis of GPS Probe Data Latency

2018 ◽  
Vol 2672 (42) ◽  
pp. 180-191 ◽  
Author(s):  
Zhongxiang Wang ◽  
Masoud Hamedi ◽  
Elham Sharifi ◽  
Stanley Young
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Ground Truth ◽  
Traffic Information ◽  
Incident Detection ◽  
Dynamic Message Signs ◽  
Real Time Traffic ◽  
Outsourced Data ◽  
Message Signs

Crowd sourced GPS probe data have become a major source of real-time traffic information applications. In addition to traditional traveler advisory systems such as dynamic message signs (DMS) and 511 systems, probe data are being used for automatic incident detection, integrated corridor management (ICM), end of queue warning systems, and mobility-related smartphone applications. Several private sector vendors offer minute by minute network-wide travel time and speed probe data. The quality of such data in terms of deviation of the reported travel time and speeds from ground-truth has been extensively studied in recent years, and as a result concerns over the accuracy of probe data have mostly faded away. However, the latency of probe data—defined as the lag between the time at which disturbance in traffic speed is reported in the outsourced data feed, and the time at which the traffic is perturbed—has become a subject of interest. The extent of latency of probe data for real-time applications is critical, so it is important to have a good understanding of the amount of latency and its influencing factors. This paper uses high-quality independent Bluetooth/Wi-Fi re-identification data collected on multiple freeway segments in three different states, to measure the latency of the vehicle probe data provided by three major vendors. The statistical distribution of the latency and its sensitivity to speed slowdown and recovery periods are discussed.

Link Flow Evaluation Using Loop Detector Data: Traveler Response to Variable-Message Signs

1996 ◽  
Vol 1550 (1) ◽  
pp. 58-64 ◽  
Author(s):  
Youngbin Yim ◽  
Jean-Luc Ygnace
Keyword(s):  
Real Time ◽  
Traffic Management ◽  
Preliminary Investigation ◽  
Traffic Information ◽  
Mean Flow ◽  
Variable Message Signs ◽  
Real Time Traffic ◽  
Operational Test ◽  
Paris Region ◽  
Message Signs

Système d'Information Routière Intelligible aux Usagers (SIRIUS) is the largest urban field operational test of the advanced traveler information and automated traffic management system in Europe. With variable-message signs, SIRIUS has been in operation in the Paris region for 3 years. A preliminary investigation of the effectiveness of the SIRIUS system in traffic management is presented. The extent to which drivers respond to real-time traffic information and the consequential changes in link flow under SIRIUS is also presented. Time-series traffic data were analyzed to measure changes in mean flow rates at a selected link. It was found that variable-message signs influence drivers to choose less congested routes when drivers are provided with real-time traffic information, and that a driver's decision to divert is closely associated with the information pertaining to the level of congestion. In the Paris region, drivers received information on the length of the queue at the time of this study. As congestion becomes heavier, drivers are more likely to respond to variable-message signs. According to the data analysis, a queue length of 3 km seems to be a threshold at which a significant number of drivers choose to use an alternative route.

scholarly journals The Optimal Sampling Period of a Fingerprint Positioning Algorithm for Vehicle Speed Estimation

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Ding-Yuan Cheng ◽  
Chi-Hua Chen ◽  
Chia-Hung Hsiang ◽  
Chi-Chun Lo ◽  
Hui-Fei Lin ◽  
...  
Keyword(s):  
Real Time ◽  
Intelligent Transportation Systems ◽  
Sampling Period ◽  
Transportation Systems ◽  
Analytical Models ◽  
Traffic Information ◽  
Optimal Sampling ◽  
Vehicle Speed ◽  
Real Time Traffic ◽  
Positioning Algorithm

Using cellular floating vehicle data is a crucial technique for measuring and forecasting real-time traffic information based on anonymously sampling mobile phone positions for intelligent transportation systems (ITSs). However, a high sampling frequency generates a substantial load for ITS servers, and traffic information cannot be provided instantly when the sampling period is long. In this paper, two analytical models are proposed to analyze the optimal sampling period based on communication behaviors, traffic conditions, and two consecutive fingerprint positioning locations from the same call and estimate vehicle speed. The experimental results show that the optimal sampling period is 41.589 seconds when the average call holding time was 60 s, and the average speed error rate was only 2.87%. ITSs can provide accurate and real-time speed information under lighter loads and within the optimal sampling period. Therefore, the optimal sampling period of a fingerprint positioning algorithm is suitable for estimating speed information immediately for ITSs.

Dynamic Aspects of Commuter Decisions Under Advanced Traveler Information Systems: Modeling Framework and Experimental Results

1998 ◽  
Vol 1645 (1) ◽  
pp. 111-119 ◽  
Author(s):  
Yu-Hsin Liu ◽  
Hani S. Mahmassani
Keyword(s):  
Real Time ◽  
Switching Behavior ◽  
Traffic Information ◽  
Advanced Traveler Information Systems ◽  
Traveler Information ◽  
Traveler Information Systems ◽  
Real Time Traffic ◽  
Departure Time ◽  
Real Time Information ◽  
Time Information

Previous work on the effect of advanced traveler information systems was concerned primarily with immediate route choice decisions in response to real-time traffic information. Real-time traffic information also influences day-to-day decisions of trip makers, including departure time and route choices. Joint departure time decision and pretrip route selection are addressed, as well as en route path switching behavior by commuters under real-time information availability. Data were used from laboratory experiments using a dynamic interactive traveler simulator that allows actual commuters to simultaneously interact with each other within a simulated traffic corridor. Given real-time information provided by the system, commuters determine their departure time and route at the origin and select paths en route at various decision nodes along the trip. Day-to-day dynamic models of commuters’ joint departure time and route switching decisions are developed and calibrated by using a multinomial probit model framework that takes into account commuters’ learning from experience. The analysis provides insight into day-to-day effects of real-time traffic information on user decisions. Results indicate that the reliability of real-time information and supplied schedule delay (relative to the commuters’ preferred arrival time) are significant variables that influence users’ indifference band governing route switching behavior both pretrip and en route. These models are intended for use within evaluation frameworks (e.g., simulation-assignment models). In addition, the substantive insights provide guidelines for the design of real-time information content and systems.

Traffic Signal Control for a Single Intersection-Based Intelligent Transportation System

2020 ◽  
pp. 159-180
Author(s):  
Nouha Rida ◽  
Mohammed Ouadoud ◽  
Abderrahim Hasbi
Keyword(s):  
Real Time ◽  
Waiting Time ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Traffic Information ◽  
Traffic Signal Control ◽  
Traffic Light ◽  
The Road ◽  
Real Time Traffic

Traffic optimization at an intersection, using real-time traffic information, presents an important focus of research into intelligent transportation systems. Several studies have proposed adaptive traffic lights control, which concentrates on determining green light length and sequence of the phases for each cycle in accordance with the real-time traffic detected. In order to minimize the waiting time at the intersection, the authors propose an intelligent traffic light using the information collected by a wireless sensors network installed in the road. The proposed algorithm is essentially based on two parameters: the waiting time in each lane and the length of its queue. The simulations show that the algorithm applied at a network of intersections improves significantly the average waiting time, queue length, fuel consumption, and CO2 emissions.

Four-Dimensional Interactive Visualization System for Transportation Management and Traveler Information

2005 ◽  
Vol 1937 (1) ◽  
pp. 152-158 ◽  
Author(s):  
Michael L. Pack ◽  
Phillip Weisberg ◽  
Sujal Bista
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Traffic Management ◽  
Prototype System ◽  
Traveler Information ◽  
Visualization System ◽  
Dynamic Message Signs ◽  
Traffic Sensors ◽  
Real Time Traffic ◽  
Message Signs

This research developed a system for visualizing four-dimensional (4-D), real-time transportation data for the major road networks of Washington, D.C., Northern Virginia, and the entire state of Maryland. The effort employed a combination of OpenGL and other modeling techniques to develop a scalable, highly interactive 4-D model using available geographic information system (GIS) and transportation infrastructure data in conjunction with real-time traffic management center data. The prototype system interacts with real-time traffic databases to show animations of real-time traffic data (volume and speed) along with incident data (accident locations, lane closures, responding agencies, etc.). A user can “fly” or “drive” through the region to inspect conditions at an infinite number of angles and distances. The program also allows users to monitor the status of and interact with traffic control devices such as dynamic message signs, closed-circuit television feeds, and traffic sensors and even view the location of emergency response vehicles equipped with Global Positioning System transceivers. Because the system uses standard GIS data and relatively standard transportation databases to derive traffic measures, it can be scaled to incorporate other states and agencies.

The Method of Background Extraction and Background Updating Based on Traffic Video

2013 ◽  
Vol 411-414 ◽  
pp. 1299-1304
Author(s):  
Chao Han ◽  
Jia Hao Deng ◽  
Min Han
Keyword(s):  
Real Time ◽  
Intelligent Transportation Systems ◽  
Mean Value ◽  
Transportation Systems ◽  
Traffic Information ◽  
Real Time Traffic ◽  
Moving Vehicles ◽  
Background Extraction ◽  
Background Update ◽  
Mean Value Method

In order to improve the degree and real-time of the vehicle image detection, a background extraction method based on the probability mean value method and the background update based on the weighted coefficient method through divided area are proposed through the acquisition of real-time traffic information and processing of video images for intelligent transportation systems. Finally a prototype of background extraction and background update is got, and it achieves the detection of moving vehicles. The experimental results show that this method is simple, small amount of calculation and it has a good robustness; it can extract a good background image quickly and detect a complete shadow of vehicles. So this method can meet the requirements of real-time detection of multiple moving targets.

scholarly journals The Multi-Station Based Variable Speed Limit Model for Realization on Urban Highway

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 801 ◽  
Author(s):  
Soobin Jeon ◽  
Chongmyung Park ◽  
Dongmahn Seo
Keyword(s):  
Real Time ◽  
Transportation Systems ◽  
Traffic Information ◽  
Transport Systems ◽  
Speed Limit ◽  
Stability Assessment ◽  
Variable Speed ◽  
Variable Speed Limit ◽  
Traffic Conditions ◽  
Real Time Traffic

Intelligent transport systems (ITS) are a convergence of information technology and transportation systems as seen in the variable speed limit (VSL) system. Since the VSL system controls the speed limit according to the traffic conditions, it can improve the safety and efficiency of a transport network. Many researchers have studied the real-time VSL (RVSL) algorithm based on real-time traffic information from multiple stations recording traffic data. However, this method can suffer from inaccurate selection of the VSL start station (VSS), incorrect VSL calculations, and is unable to quickly react to the changing traffic conditions. Unstable VSL systems result in more congestion on freeways. In this study, an enhanced VSL algorithm (EVSL) is proposed to address the limitations of the existing RVSL algorithm. This selects preliminary VSL start stations (pVSS), which is expected to end congestion using acceleration and allocates final VSSs for each congestion interval using selected pVSS. This controls the vehicles that entered the congestion area based on the selected VSS. We used four metrics to evaluate the performance of the proposed VSL (VSS stability assessment, speed control stability assessment, travel time, and shockwave), which were all enhanced when compared to the standard RVSL algorithm. In addition, the EVSL algorithm showed stable VSL performance, which is critical for road safety.

PENGEMBANGAN ”REAL TIME TRAFFIC INFORMATION SYSTEM” BAGI PENGGUNA JALAN

2013 ◽  
Vol 12 (3) ◽  
Author(s):  
Rusmadi Suyuti
Keyword(s):  
Information System ◽  
Real Time ◽  
Travel Demand ◽  
Urban Traffic ◽  
Road User ◽  
Traffic Information ◽  
Estimation Methods ◽  
Traffic Counts ◽  
Real Time Traffic ◽  
Demand Models

Traffic information condition is a very useful  information for road user because road user can choose his best route for each trip from his origin to his destination. The final goal for this research is to develop real time traffic information system for road user using real time traffic volume. Main input for developing real time traffic information system is an origin-destination (O-D) matrix to represent the travel pattern. However, O-D matrices obtained through a large scale survey such as home or road side interviews, tend to be costly, labour intensive and time disruptive to trip makers. Therefore, the alternative of using traffic counts to estimate O-D matrices is particularly attractive. Models of transport demand have been used for many years to synthesize O-D matrices in study areas. A typical example of the approach is the gravity model; its functional form, plus the appropriate values for the parameters involved, is employed to produce acceptable matrices representing trip making behaviour for many trip purposes and time periods. The work reported in this paper has combined the advantages of acceptable travel demand models with the low cost and availability of traffic counts. Two types of demand models have been used: gravity (GR) and gravity-opportunity (GO) models. Four estimation methods have been analysed and tested to calibrate the transport demand models from traffic counts, namely: Non-Linear-Least-Squares (NLLS), Maximum-Likelihood (ML), Maximum-Entropy (ME) and Bayes-Inference (BI). The Bandung’s Urban Traffic Movement survey has been used to test the developed method. Based on several statistical tests, the estimation methods are found to perform satisfactorily since each calibrated model reproduced the observed matrix fairly closely. The tests were carried out using two assignment techniques, all-or-nothing and equilibrium assignment.  

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