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%.

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).

Arterial Travel Time Validation and Augmentation with Two Independent Data Sources

2015 ◽  
Vol 2526 (1) ◽  
pp. 79-89 ◽  
Author(s):  
Xuechi Zhang ◽  
Masoud Hamedi ◽  
Ali Haghani
Keyword(s):  
Travel Time ◽  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Multiple Sources ◽  
Time Data ◽  
Travel Time Data ◽  
Time Information ◽  
Fusion Framework ◽  
Validation Scheme

Travel time data are a key input to applications of intelligent transportation systems. Advancement in vehicle tracking and reidentification technologies and proliferation of location-aware and connected devices have made networkwide travel time data available to transportation management agencies. The trend started with data collection on freeways and has been quickly extended to arterials. Although the freeway travel time data have been validated extensively in recent years, the quality of arterial travel time data is not well known. This paper presents a comprehensive validation scheme for arterial travel time data based on GPS probe and Bluetooth data as two independent sources. Since travel time on arterials is subject to a higher degree of variation than that on freeways, mainly because of the presence of signals, a new validation methodology based on the coefficient of variation is introduced. Moreover, a context-dependent travel time fusion framework is developed to improve the reliability of travel time information by fusing data from multiple sources. All 2012 data from a busy arterial corridor in Maryland are used to demonstrate the proposed comparison and augmentation model.

Seismic wave travel times from nuclear explosions

1958 ◽  
Vol 48 (4) ◽  
pp. 377-398
Author(s):  
Dean S. Carder ◽  
Leslie F. Bailey
Keyword(s):  
Travel Time ◽  
Travel Times ◽  
Strong Variation ◽  
Time Data ◽  
Owens Valley ◽  
Central California ◽  
Nuclear Explosions ◽  
Travel Time Data ◽  
The Pacific ◽  
Wave Travel

Abstract A large number of seismograph records from nuclear explosions in the Nevada and Pacific Island proving grounds have been collected and analyzed. The Nevada explosions were well recorded to distances of 6°.5 (450 mi.) and weakly recorded as far as 17°.5, and under favorable circumstances as far as 34°. The Pacific explosions had world-wide recording except that regional data were necessarily meager. The Nevada data confirm that the crustal thickness in the area is about 35 km., with associations of 6.1 km/sec. speeds in the crust and 8.0 to 8.2 km/sec. speeds beneath it. They indicate that there is no uniform layering in the crust, and that if higher-speed media do exist, they are not consistent; also, that the crust between the proving grounds and central California shows a thickening probably as high as 70 or 75 km., and that this thickened portion may extend beneath the Owens Valley. The data also point to a discontinuity at postulated depths of 160 to 185 km. Pacific travel times out to 14° are from 4 to 8 sec. earlier than similar continental data partly because of a thinner crust, 17 km. or less, under the atolls and partly because speeds in the top of the mantle are more nearly 8.15 km/sec. than 8.0 km/sec. More distant points, at 17°.5 and 18°.5, indicate slower travel times—about 8.1 km/sec. A fairly sharp discontinuity at 19° in the travel-time data is indicated. Travel times from Pacific sources to North America follow closely Jeffreys-Bullen 1948 and Gutenberg 1953 travel-time curves for surface foci except they are about 2 sec. earlier on the continent, and Arctic and Pacific basin data are about 2 sec. still earlier. The core reflection PcP shows a strong variation in amplitude with slight changes in distance at two points where sufficient data were available.

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.

Optimizing Aggregation Level for Intelligent Transportation System Data Based on Wavelet Decomposition

2003 ◽  
Vol 1840 (1) ◽  
pp. 10-20 ◽  
Author(s):  
Fengxiang Qiao ◽  
Xin Wang ◽  
Lei Yu
Keyword(s):  
Intelligent Transportation Systems ◽  
Wavelet Decomposition ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Data Sets ◽  
Time Data ◽  
Aggregated Data ◽  
A New Technique ◽  
Aggregation Level

Appropriate aggregation levels and sampling frames of real-time data in intelligent transportation systems (ITSs) are indispensable to transportation planners and engineers. Conventional techniques for the retrieval of aggregation levels are normally based on statistical comparison of the original and the aggregated data sets. However, it is not guaranteed that errors and noise will not be transmitted to the aggregated data sets and that the desired information will be reserved. Wavelet decomposition is a new technique that can be applied to the determination of aggregation level. An optimization process that can provide the optimized aggregation level of ITS data for different applications was developed. To illustrate the proposed technique, ITS data archived by the TransGuide Center in San Antonio, Texas, were used for a case study. Aggregation levels for different days of a week and different time periods over the whole year of 2001 were obtained through the proposed approach.

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.

Travel time estimation in urban networks using limited probes data

2011 ◽  
Vol 38 (3) ◽  
pp. 305-318 ◽  
Author(s):  
Mohamed El Esawey ◽  
Tarek Sayed
Keyword(s):  
Simulation Model ◽  
Travel Time ◽  
Network Performance ◽  
Time Estimation ◽  
Performance Measure ◽  
Estimation Accuracy ◽  
Travel Time Estimation ◽  
Travel Times ◽  
Time Data ◽  
Travel Time Data

Travel time is a simple and robust network performance measure that is well understood by the public. However, travel time data collection can be costly especially if the analysis area is large. This research proposes a solution to the problem of limited network sensor coverage caused by insufficient sample size of probe vehicles or inadequate numbers of fixed sensors. Within a homogeneous road network, nearby links of similar character are exposed to comparable traffic conditions, and therefore, their travel times are likely to be positively correlated. This correlation can be useful in developing travel time relationships between nearby links so that if data becomes available on a subset of these links, travel times of their neighbours can be estimated. A methodology is proposed to estimate link travel times using available data from neighbouring links. To test the proposed methodology, a case study was undertaken using a VISSIM micro-simulation model of downtown Vancouver. The simulation model was calibrated and validated using field traffic volumes and travel time data. Neighbour links travel time estimation accuracy was assessed using different error measurements and the results were satisfactory. Overall, the results of this research demonstrate the feasibility of using neighbour links data as an additional source of information to estimate travel time, especially in case of limited coverage.

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