scholarly journals Multi-Sensor Data Fusion for Accurate Traffic Speed and Travel Time Reconstruction

2021 ◽  
Vol 2 ◽  
Author(s):  
Lisa Kessler ◽  
Felix Rempe ◽  
Klaus Bogenberger
Keyword(s):  
Travel Time ◽  
Sensor Data ◽  
Estimation Accuracy ◽  
The Novel ◽  
Travel Times ◽  
Time Data ◽  
Reconstruction Methods ◽  
Multi Sensor Data Fusion ◽  
Novel Approach ◽  
Time Accuracy

This paper studies the joint reconstruction of traffic speeds and travel times by fusing sparse sensor data. Raw speed data from inductive loop detectors and floating cars as well as travel time measurements are combined using different fusion techniques. A novel fusion approach is developed, which extends existing speed reconstruction methods to integrate low-resolution travel time data. Several state-of-the-art methods and the novel approach are evaluated on their performance in reconstructing traffic speeds and travel times using various combinations of sensor data. Algorithms and sensor setups are evaluated with real loop detector, floating car and Bluetooth data collected during severe congestion on German freeway A9. Two main aspects are examined: 1) which algorithm provides the most accurate result depending on the used data and 2) which type of sensor and which combination of sensors yields highest estimation accuracy. Results show that, overall, the novel approach applied to a combination of floating-car data and loop data provides the best speed and travel time accuracy. Furthermore, a fusion of sources improves the reconstruction quality in many, but not all cases. In particular, Bluetooth data only provide a benefit for reconstruction purposes if integrated subtly.

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.

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.

Meaningful Modeling of Section Bus Running Times by Time Varying Mixture Distributions of Fixed Components

2020 ◽  
Vol 2674 (8) ◽  
pp. 626-637
Author(s):  
Beda Büchel ◽  
Francesco Corman
Keyword(s):  
Travel Time ◽  
Explanatory Power ◽  
Time Varying ◽  
Time Data ◽  
Running Time ◽  
Novel Approach ◽  
Travel Time Data ◽  
Conventional Models ◽  
Bus Travel Time ◽  
Transit Reliability

Understanding the variability of bus travel time is a key issue in the optimization of schedules, transit reliability, route choice analysis, and transit simulation. The statistical modeling of bus travel time data is of increasing importance given the increasing availability of data. In this paper, we introduce a novel approach to modeling the day-to-day variability of urban bus running times on a section level. First, the explanatory power of conventionally used distributions is examined, based on likelihood and effect size. We show that a mixture model is a powerful tool to increase fitting performance, but the applied components need to be justified. To overcome this issue, we propose a novel model consisting of two individual characteristic distributions representing either off-peak or peak hour dynamics. The observed running time distribution at every hour of the day can be described as a combination (mixture) of the two dynamics. The proposed time varying model uses a small set of parameters, which are physically interpretable and capable of accurately describing running time distributions. With our modeling approach, we reduce the complexity of mixture models and increase the explanatory power and fit compared with conventional models.

Evaluation of Online Travel Time Estimators and Predictors

2000 ◽  
Vol 1719 (1) ◽  
pp. 45-53 ◽  
Author(s):  
Charles D. R. Lindveld ◽  
Remmelt Thijs ◽  
Piet H. L. Bovy ◽  
Nanne J. Van der Zijpp
Keyword(s):  
Travel Time ◽  
Traffic Management ◽  
Negative Impact ◽  
Test Site ◽  
Estimation Accuracy ◽  
Travel Times ◽  
Congested Traffic ◽  
Traffic Conditions ◽  
Loop Detectors ◽  
Online Travel

Travel time is an important characteristic of traffic conditions in a road network. Up-to-date travel time information is important in dynamic traffic management. Presented are the findings of a recently completed research and evaluation program called DACCORD, regarding the evaluation of tools for online estimation and prediction of travel times by using induction loop detector data. Many methods exist with which to estimate and predict travel time by using induction loop data. Several of these methods were implemented and evaluated in three test sites in France, Italy, and the Netherlands. Both cross-tool and cross-site evaluations have been carried out. Travel time estimators based on induction loop detectors were evaluated against observed travel times and were seen to be reasonably accurate (10 percent to 15 percent root mean square error proportional) across different sites for uncongested to lightly congested traffic conditions. The evaluation period varied by site from 4 to 30 days. Results were seen to diverge at higher congestion levels: at one test site, congestion levels were seen to have a strong negative impact on estimation accuracy; at another test site, accuracy was maintained even in congested conditions.

scholarly journals Shear-wave travel times and amplitudes for two well-constrained earthquakes

1978 ◽  
Vol 68 (4) ◽  
pp. 973-985
Author(s):  
Robert S. Hart ◽  
Rhett Butler
Keyword(s):  
Travel Time ◽  
Shear Wave ◽  
Correlation Technique ◽  
Travel Times ◽  
Time Data ◽  
Data Set ◽  
A Value ◽  
Travel Time Data ◽  
Distance Dependence ◽  
Wave Travel

abstract The wave-form correlation technique (Hart, 1975) for determining precise teleseismic shear-wave travel times is extended to two large earthquakes with well-constrained source mechanisms, the 1968 Borrego Mountain, California earthquake and the 1973 Hawaii earthquake. A total of 87 SH travel times in the distance range of 30° to 92° were obtained through analysis of WWSSN and Canadian Network seismograms from these two events. Major features of the travel-time data include a trend toward faster travel times at a distance of about 40° (previously noted by Ibrahim and Nuttli, 1967; Hart, 1975); another somewhat less pronounced trend toward faster times at about 75°; a +6 sec base line shift, with respect to the Jeffreys-Bullen Table, for the Borrego Mountain data; and large azimuthally-dependent scatter for the Hawaiian data, probably reflecting dramatic lateral variations in the near-source region. When azimuthal variations in the Hawaii data are removed, the travel times from both events show very low scatter. The correlations were also used to investigate SH amplitudes for possible distance dependence in the data and variations in tβ*. The Borrego Mountain data show very low scatter and no discernible distance dependence. All of the data are compatible with a value of tβ* = 5.2 ± 0.5. The amplitudes from the Hawaii earthquake show the same azimuthal variations found in the travel-time data. When those effects are removed, the Hawaii data satisfies a value of tβ* equal to 4.0 ± 0.5 and, as with the other data set, no distance dependence is apparent.

scholarly journals Estimation of Travel Times for Minor Roads in Urban Areas Using Sparse Travel Time Data

2020 ◽  
pp. 1-1
Author(s):  
Luong H. Vu ◽  
Benjamin N. Passow ◽  
Daniel Paluszczyszyn ◽  
Lipika Deka ◽  
Eric Goodyer
Keyword(s):  
Travel Time ◽  
Urban Areas ◽  
Travel Times ◽  
Time Data ◽  
Travel Time Data

scholarly journals Reliable Estimation of Urban Link Travel Time Using Multi-Sensor Data Fusion

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 267
Author(s):  
Yajuan Guo ◽  
Licai Yang
Keyword(s):  
Data Fusion ◽  
Travel Time ◽  
Sensor Data ◽  
License Plate ◽  
Traffic Data ◽  
Characteristic Analysis ◽  
Reliable Estimation ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Link Travel Time

Travel time is one of the most critical indexes to describe urban traffic operating states. How to obtain accurate and robust travel time estimates, so as to facilitate to make traffic control decision-making for administrators and trip-planning for travelers, is an urgent issue of wide concern. This paper proposes a reliable estimation method of urban link travel time using multi-sensor data fusion. Utilizing the characteristic analysis of each individual traffic sensor data, we first extract link travel time from license plate recognition data, geomagnetic detector data and floating car data, respectively, and find that their distribution patterns are similar and follow logarithmic normal distribution. Then, a support degree algorithm based on similarity function and a credibility algorithm based on membership function are developed, aiming to overcome the conflicts among multi-sensor traffic data and the uncertainties of single-sensor traffic data. The reliable fusion weights for each type of traffic sensor data are further determined by integrating the corresponding support degree with credibility. A case study was conducted using real-world data from a link of Jingshi Road in Jinan, China and demonstrated that the proposed method can effectively improve the accuracy and reliability of link travel time estimations in urban road systems.

Improving Interstate Freeway Travel Time Reliability Analysis by Clustering Travel Time Distributions

2021 ◽  
pp. 036119812110120
Author(s):  
Xiaoxiao Zhang ◽  
Mo Zhao ◽  
Justice Appiah ◽  
Michael D. Fontaine
Keyword(s):  
Travel Time ◽  
Cumulative Distribution ◽  
Travel Time Reliability ◽  
Travel Times ◽  
Clustering Methods ◽  
Time Data ◽  
Dissimilarity Matrix ◽  
Model Based ◽  
Probe Vehicle ◽  
Vehicle Data

Travel time reliability quantifies variability in travel times and has become a critical aspect for evaluating transportation network performance. The empirical travel time cumulative distribution function (CDF) has been used as a tool to preserve inherent information on the variability and distribution of travel times. With advances in data collection technology, probe vehicle data has been frequently used to measure highway system performance. One challenge with using CDFs when handling large amounts of probe vehicle data is deciding how many different CDFs are necessary to fully characterize experienced travel times. This paper explores statistical methods for clustering CDFs of travel times at segment level into an optimal number of homogeneous clusters that retain all relevant distributional information. Two clustering methods were tested, one based on classic hierarchical clustering and the other used model-based functional data clustering, to find out their performance on clustering distributions using travel time data from Interstate 64 in Virginia. Freeway segments and those within interchange areas were clustered separately. To find the proper data format as clustering input, both scaled and original travel times were considered. In addition, a non-data-driven method based on geometric features was included for comparison. The results showed that for freeway segments, clustering using travel times and the Anderson–Darling dissimilarity matrix and Ward’s linkage had the best performance. For interchange segments, model-based clustering provided the best clusters. By clustering segments into homogenous groups, the results of this study could improve the efficiency of further travel time reliability modeling.

scholarly journals Calibrating Microsimulation Parameters for Vehicular Travel Time

2021 ◽  
Vol 9 (5) ◽  
pp. 565-569
Keyword(s):  
Travel Time ◽  
Simulation Models ◽  
Congestion Management ◽  
Traffic Network ◽  
Travel Times ◽  
Time Data ◽  
Urban Road ◽  
Speed Distribution ◽  
Travel Time Data ◽  
Calibration Parameters

Travel time is one of the simplest yet the most important parameter for transportation facility users as well as transportation engineers. Travel time data is valuable for widerange of transportation analysis including congestion management, transportation planning and passenger’sdecision making.Traffic simulation models are now becoming necessary tools to understand the behavior of traffic and reduce vehicular travel times, but it is very important to calibrate these models first. Thisstudy attempts to determines the values of those parameters,using microsimulation,that significantly affect the travel time. These parameters arethenused for calibrating the traffic simulation model that results in realistic travel time.Study was conducted on an urban road andfield data was collected during weekdays for peak hours. The traffic network was modelled usingVISSIM®.The calibration parameters were desired speed distribution, number of lanes,average standstill distance and minimum headway. After calibrating the model, the travel times collected from field data and those by simulations for different modes of transportation were in close agreement.

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%.

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