Travel-Time Estimates Obtained from Intelligent Transportation Systems and Instrumented Test Vehicles: Statistical Comparison

2002 ◽  
Vol 1804 (1) ◽  
pp. 8-16 ◽  
Author(s):  
William L. Eisele ◽  
Laurence R. Rilett
Keyword(s):  
Travel Time ◽  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Data Sources ◽  
Travel Times ◽  
System Monitoring ◽  
Test Vehicle ◽  
Cellular Telephones ◽  
Mobile Cellular ◽  
Time Estimates

Accurate estimation of travel time is necessary for monitoring the performance of the transportation system. Often, travel times are estimated indirectly by using instantaneous speeds from inductance loop detectors and making a number of assumptions. Although these travel times may be acceptable estimates for uncongested conditions, they may have significant error during congested periods. Travel times also may be obtained directly from intelligent transportation systems (ITS) data sources such as automatic vehicle identification (AVI). In addition, mobile cellular telephones have been touted as a means for obtaining this information automatically. Data sources that collect travel-time estimates directly provide travel-time data for both real-time and off-line transportation system monitoring. Instrumented test vehicle runs are often performed to obtain travel-time estimates for system monitoring and other transportation applications. Distance measuring instruments (DMIs) are a common method of instrumentation for test vehicles. DMI travel-time estimates are compared with AVI travel-time estimates by using a variety of statistical approaches. The results indicate that the travel-time estimates from test vehicles instrumented with DMI are within 1% of travel-time estimates from AVI along the study corridor. These results reflect that DMI is an accurate instrumented test vehicle technology and, more important, AVI data sources can replace traditional system monitoring data collection methods when there is adequate tag penetration and infrastructure. A method for identifying instrumented test vehicle drivers who may require additional data collection training is provided. The described procedures are applicable to any instrumented vehicle technique (e.g., the Global Positioning System) in comparison to any ITS data source that directly estimates travel time (e.g., mobile cellular telephones).

scholarly journals Ann Modeling For Predicting Car Travel Time using Bus As Probe

2019 ◽  
Vol 8 (12) ◽  
pp. 3260-3265
Keyword(s):  
Travel Time ◽  
Intelligent Transportation Systems ◽  
Critical Issue ◽  
Transportation Systems ◽  
Travel Times ◽  
Time Data ◽  
Comparison Results ◽  
Greater Cairo ◽  
Artificial Neural Network Ann ◽  
Car Travel

The critical issue of Intelligent Transportation Systems (ITS) applications is obtaining the near real time information of travel times. This paper proposes a dependable model for predicting car travel time on urban roads in Greater Cairo using buses as probes. The GPS receivers, which are installed on test vehicles and buses, used to collect real travel time data along the urban roads. The travel times of bus and car are compared in order to recognize similarities and differences between the trip profiles of test vehicles and buses. According to the comparison results, the model is developed and validated using Artificial Neural Network (ANN) for estimating car travel time using buses’ travel time with acceptable level of accuracy equals 10.53%.

scholarly journals The Effect of Route-choice Strategy on Transit Travel Time Estimates

2019 ◽  
Author(s):  
Nate Wessel ◽  
Steven Farber
Keyword(s):  
Travel Time ◽  
Shortest Path ◽  
Imperfect Information ◽  
Optimal Path ◽  
Shortest Paths ◽  
Route Choice ◽  
Travel Times ◽  
Selection Strategies ◽  
Time Estimates ◽  
Average Travel Time

Estimates of travel time by public transit often rely on the calculation of a shortest-path between two points for a given departure time. Such shortest-paths are time-dependent and not always stable from one moment to the next. Given that actual transit passengers necessarily have imperfect information about the system, their route selection strategies are heuristic and cannot be expected to achieve optimal travel times for all possible departures. Thus an algorithm that returns optimal travel times at all moments will tend to underestimate real travel times all else being equal. While several researchers have noted this issue none have yet measured the extent of the problem. This study observes and measures this effect by contrasting two alternative heuristic routing strategies to a standard shortest-path calculation. The Toronto Transit Commission is used as a case study and we model actual transit operations for the agency over the course of a normal week with archived AVL data transformed into a retrospective GTFS dataset. Travel times are estimated using two alternative route-choice assumptions: 1) habitual selection of the itinerary with the best average travel time and 2) dynamic choice of the next-departing route in a predefined choice set. It is shown that most trips present passengers with a complex choice among competing itineraries and that the choice of itinerary at any given moment of departure may entail substantial travel time risk relative to the optimal outcome. In the context of accessibility modelling, where travel times are typically considered as a distribution, the optimal path method is observed in aggregate to underestimate travel time by about 3-4 minutes at the median and 6-7 minutes at the \nth{90} percentile for a typical trip.

Calibration of Microsimulation Models Using Nonparametric Statistical Techniques

2005 ◽  
Vol 1935 (1) ◽  
pp. 111-119 ◽  
Author(s):  
Seung-Jun Kim ◽  
Wonho Kim ◽  
L. R. Rilett
Keyword(s):  
Travel Time ◽  
Intelligent Transportation Systems ◽  
Absolute Error ◽  
Calibration Procedure ◽  
Transportation Systems ◽  
Related Data ◽  
Kolmogorov Smirnov ◽  
The Mean ◽  
Simulation Results ◽  
Microsimulation Models

The calibration of traffic microsimulation models has received widespread attention in transportation modeling. A recent concern is whether these models can simulate traffic conditions realistically. The recent widespread deployment of intelligent transportation systems in North America has provided an opportunity to obtain traffic-related data. In some cases the distribution of the traffic data rather than simple measures of central tendency such as the mean, is available. This paper examines a method for calibrating traffic microsimulation models so that simulation results, such as travel time, represent observed distributions obtained from the field. The approach is based on developing a statistically based objective function for use in an automated calibration procedure. The Wilcoxon rank–sum test, the Moses test and the Kolmogorov–Smirnov test are used to test the hypothesis that the travel time distribution of the simulated and the observed travel times are statistically identical. The approach is tested on a signalized arterial roadway in Houston, Texas. It is shown that potentially many different parameter sets result in statistically valid simulation results. More important, it is shown that using simple metrics, such as the mean absolute error, may lead to erroneous calibration results.

Simulation of an Arterial Incident Environment with Probe Reporting Capability

1998 ◽  
Vol 1644 (1) ◽  
pp. 116-123 ◽  
Author(s):  
Natacha Thomas ◽  
Bader Hafeez
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Simulation ◽  
Traffic Monitoring ◽  
Transportation Systems ◽  
Incident Detection ◽  
Travel Times ◽  
Control Center ◽  
Microscopic Traffic Simulation ◽  
Arterial Streets ◽  
Detection Systems

Intelligent transportation systems have created new traffic monitoring approaches and fueled new interests in automated incident detection systems. One new monitoring approach utilizes actual travel times experienced by vehicles, called probes, equipped to transmit this information in real time to a control center. The database needed to design and calibrate arterial incident detection systems based on probe travel times is nonexistent. A microscopic traffic simulation package, Integrated Traffic Simulation, was selected and enhanced to generate vehicle travel times for the incident and incident-free conditions on an arterial. We evaluated the enhanced model. Significant variations in probe travel times were observed in the event of incidents. Average travel time, contrary to average occupancy, may increase, decrease, or remain constant on arterial streets downstream of an incident.

Impact of Travel Time Models on Quality of Real-Time Routing Instructions

2003 ◽  
Vol 1857 (1) ◽  
pp. 21-29 ◽  
Author(s):  
Elise Miller-Hooks ◽  
Baiyu Yang
Keyword(s):  
Real Time ◽  
Intelligent Transportation Systems ◽  
Communication Systems ◽  
Service Providers ◽  
A Priori ◽  
Information Service ◽  
Small Neighborhood ◽  
Transportation Systems ◽  
Travel Times ◽  
Real Time Information

Mobile communication systems coupled with intelligent transportation systems technologies can permit information service providers to supply real-time routing instructions to suitably equipped vehicles as real-time travel times are received. Simply considering current conditions in updating routing decisions, however, may lead to suboptimal path choices, because future travel conditions likely will differ from that currently observed. Even with perfect and continuously updated information about current conditions, future travel times can be known a priori with uncertainty at best. Further, in congested transportation systems, conditions vary over time as recurrent congestion may change with a foreseeable pattern during peak driving hours. It is postulated that better, more robust routing instructions can be provided by explicitly accounting for this inherent stochastic and dynamic nature of future travel conditions in generating the routing instructions. It is further hypothesized that nearly equally good routing instructions can be provided by collecting real-time information from only a small neighborhood within the transportation system as from the entire system. Extensive numerical experiments were conducted to assess the validity of these two hypotheses.

scholarly journals Intelligent Traffic Management: A Review of Challenges, Solutions, and Future Perspectives

2021 ◽  
Vol 22 (2) ◽  
pp. 163-182
Author(s):  
Roopa Ravish ◽  
Shanta Ranga Swamy
Keyword(s):  
Travel Time ◽  
Intelligent Transportation Systems ◽  
Traffic Management ◽  
Transportation Systems ◽  
Travel Time Prediction ◽  
Novel Technologies ◽  
Related Data ◽  
Traffic Systems ◽  
And Control ◽  
Future Work

Abstract Recent years have witnessed a colossal increase of vehicles on the roads; unfortunately, the infrastructure of roads and traffic systems has not kept pace with this growth, resulting in inefficient traffic management. Owing to this imbalance, traffic jams on roads, congestions, and pollution have shown a marked increase. The management of growing traffic is a major issue across the world. Intelligent Transportation Systems (ITS) have a great potential in offering solutions to such issues by using novel technologies. In this review, the ITS-based solutions for traffic management and control have been categorized as traffic data collection solutions, traffic management solutions, congestion avoidance solutions, and travel time prediction solutions. The solutions have been presented along with their underlying technologies, advantages, and drawbacks. First, important solutions for collecting traffic-related data and road conditions are discussed. Next, ITS solutions for the effective management of traffic are presented. Third, key strategies based on machine learning and computational intelligence for avoiding congestion are outlined. Fourth, important solutions for accurately predicting travel time are presented. Finally, avenues for future work in these areas are discussed.

IMPROVING ROAD TRAFFIC ORGANIZATION THROUGH THE APPLICATION OF INTELLIGENT TRANSPORTATION SYSTEMS

2021 ◽  
Vol 74 (3) ◽  
pp. 80-86
Author(s):  
L.E. KUSHCHENKO ◽  
◽  
A.S. KAMBUR ◽  
A.A. PEKHOV ◽  
◽  
...  
Keyword(s):  
Travel Time ◽  
Russian Federation ◽  
Intelligent Transportation Systems ◽  
Road Traffic ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Environmental Situation ◽  
The Russian Federation ◽  
Traffic Organization ◽  
The City

Examples of the use of ITS in various countries are given, improvements in traffic manage-ment, methods of reducing delays, travel time, as well as improving the environmental situation when using systems are considered. The system «Auto-Intellect», used in the territory of the Russian Federation, is presented. On the example of the city of Belgorod, a method of using ITS is pro-posed, by prohibiting the entry of cars into the city, taking into account certain state license plates.

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