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.

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.

Simplex-Based Calibration of Traffic Microsimulation Models with Intelligent Transportation Systems Data

2003 ◽  
Vol 1855 (1) ◽  
pp. 80-89 ◽  
Author(s):  
Kyu-Ok Kim ◽  
L. R. Rilett
Keyword(s):  
Intelligent Transportation Systems ◽  
Urban Areas ◽  
System Modeling ◽  
Intelligent Transportation ◽  
Simplex Algorithm ◽  
Transportation System ◽  
Transportation Systems ◽  
Test Bed ◽  
Morning Peak ◽  
Microsimulation Models

In recent years, microsimulation has become increasingly important in transportation system modeling. A potential issue is whether these models adequately represent reality and whether enough data exist with which to calibrate these models. There has been rapid deployment of intelligent transportation system (ITS) technologies in most urban areas of North America in the last 10 years. While ITSs are developed primarily for real-time traffic operations, the data are typically archived and available for traffic microsimulation calibration. A methodology, based on the sequential simplex algorithm, that uses ITS data to calibrate microsimulation models is presented. The test bed is a 23-km section of Interstate 10 in Houston, Texas. Two microsimulation models, CORSIM and TRANSIMS, were calibrated for two different demand matrices and three periods (morning peak, evening peak, and off-peak). It was found for the morning peak that the simplex algorithm had better results then either the default values or a simple, manual calibration. As the level of congestion decreased, the effectiveness of the simplex approach also decreased, as compared with standard techniques.

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.

scholarly journals Using Deep Learning to Forecast Maritime Vessel Flows

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1761 ◽  
Author(s):  
Xiangyu Zhou ◽  
Zhengjiang Liu ◽  
Fengwu Wang ◽  
Yajuan Xie ◽  
Xuexi Zhang
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Short Term Memory ◽  
Absolute Error ◽  
Transportation Systems ◽  
Traffic Information ◽  
Automatic Identification ◽  
Identification System ◽  
System Data ◽  
Lstm Network

Forecasting vessel flows is important to the development of intelligent transportation systems in the maritime field, as real-time and accurate traffic information has favorable potential in helping a maritime authority to alleviate congestion, mitigate emission of GHG (greenhouse gases) and enhance public safety, as well as assisting individual vessel users to plan better routes and reduce additional costs due to delays. In this paper, we propose three deep learning-based solutions to forecast the inflow and outflow of vessels within a given region, including a convolutional neural network (CNN), a long short-term memory (LSTM) network, and the integration of a bidirectional LSTM network with a CNN (BDLSTM-CNN). To apply those solutions, we first divide the given maritime region into M × N grids, then we forecast the inflow and outflow for all the grids. Experimental results based on the real AIS (Automatic Identification System) data of marine vessels in Singapore demonstrate that the three deep learning-based solutions significantly outperform the conventional method in terms of mean absolute error and root mean square error, with the performance of the BDLSTM-CNN-based hybrid solution being the best.

scholarly journals A review of travel and arrival-time prediction methods on road networks: classification, challenges and opportunities

2021 ◽  
Vol 7 ◽  
pp. e689
Author(s):  
Asad Abdi ◽  
Chintan Amrit
Keyword(s):  
Travel Time ◽  
Intelligent Transportation Systems ◽  
Selection Process ◽  
Influence Factors ◽  
Transportation Systems ◽  
Future Research ◽  
Extensive Literature ◽  
Travel Time Prediction ◽  
Significant Information ◽  
Time Prediction

Transportation plays a key role in today’s economy. Hence, intelligent transportation systems have attracted a great deal of attention among research communities. There are a few review papers in this area. Most of them focus only on travel time prediction. Furthermore, these papers do not include recent research. To address these shortcomings, this study aims to examine the research on the arrival and travel time prediction on road-based on recently published articles. More specifically, this paper aims to (i) offer an extensive literature review of the field, provide a complete taxonomy of the existing methods, identify key challenges and limitations associated with the techniques; (ii) present various evaluation metrics, influence factors, exploited dataset as well as describe essential concepts based on a detailed analysis of the recent literature sources; (iii) provide significant information to researchers and transportation applications developer. As a result of a rigorous selection process and a comprehensive analysis, the findings provide a holistic picture of open issues and several important observations that can be considered as feasible opportunities for future research directions.

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