Search results

The purpose of this paper is to introduce a weighted average method to process speed measurements from multiple magnetic sensors, which are installed on road segments. Speeds are weighted‐averaged in a fix duration time (5 minutes) for each sensor across location index of the sensor where it was installed. The proposed method is evaluated with numeric and simulation results.

Unlike traditional vehicle average speed measurements, the authors propose a weighted‐average speed measurement method of road segment, using wireless magnetic sensor nodes, which are installed on the measured road segment. Magnetic sensors offer a non‐contact vehicle detection method, and small sensors with relatively low power consumption. Using magnetic sensors, the local changes in the Earth's magnetic field caused by the presence of a moving vehicle can be measured and the vehicle's speed obtained. Next, using adaptive weighted average algorithm and space weighted algorithm in a fixed period, the weighted average travel speed of road segment can be obtained.

In current literature, there are many methods to measure vehicles' speed on road, such as image‐based, radar‐based, GPS‐based, double‐loop‐based or magnetic sensor‐based, but most of them only provide individual vehicle speed. Using probe vehicles, mean travel speed of road segment can be obtained, but it is costly on hardware and measurement, because many probe‐vehicles need to be used on roads and many measurements need to be done everyday. GPS data can be used to provide valuable travel speed data for Intelligent Transportation System (ITS). However, not every vehicle is equipped with GPS and to access ID numbers for personal cars would entail privacy problems. Mean travel speed of road segment is obtained based on statistical average speed. Generally, statistical average speed is used, which is based upon Gaussian distribution is not true in traffic systems.

By using wireless magnetic sensor nodes, vehicle instantaneous speeds are obtained in a fixed time when vehicles are passing over sensor nodes and then the adaptive weighted average speed on each sensor node location is computed based on the monitoring data from each sensor node in the fixed time. Considering different weights of each lane and road space (in the middle of the road segment or near the intersection), the proposed scheme can obtain the weighted‐average speed of the road segment.

Metropolitan areas suffer from frequent road traffic congestion not only during peak hours but also during off-peak periods. Different machine learning methods have been used in travel time prediction, however, such machine learning methods practically face the problem of overfitting. Tree-based ensembles have been applied in various prediction fields, and such approaches usually produce high prediction accuracy by aggregating and averaging individual decision trees. The inherent advantages of these approaches not only get better prediction results but also have a good bias-variance trade-off which can help to avoid overfitting. However, the reality is that the application of tree-based integration algorithms in traffic prediction is still limited. This study aims to improve the accuracy and interpretability of the models by using random forest (RF) to analyze and model the travel time on freeways.

As the traffic conditions often greatly change, the prediction results are often unsatisfactory. To improve the accuracy of short-term travel time prediction in the freeway network, a practically feasible and computationally efficient RF prediction method for real-world freeways by using probe traffic data was generated. In addition, the variables’ relative importance was ranked, which provides an investigation platform to gain a better understanding of how different contributing factors might affect travel time on freeways.

The parameters of the RF model were estimated by using the training sample set. After the parameter tuning process was completed, the proposed RF model was developed. The features’ relative importance showed that the variables (travel time 15 min before) and time of day (TOD) contribute the most to the predicted travel time result. The model performance was also evaluated and compared against the extreme gradient boosting method and the results indicated that the RF always produces more accurate travel time predictions.

This research developed an RF method to predict the freeway travel time by using the probe vehicle-based traffic data and weather data. Detailed information about the input variables and data pre-processing were presented. To measure the effectiveness of proposed travel time prediction algorithms, the mean absolute percentage errors were computed for different observation segments combined with different prediction horizons ranging from 15 to 60 min.

This paper aims to present collection and analysis of heterogeneous urban traffic data, and integration of them through a kernel-based approach for assessing performance of urban transport network facilities. The recent development in sensing and information technology opens up opportunities for researching the use of this vast amount of new urban traffic data. This paper contributes to analysis and management of urban transport facilities.

In this paper, the data fusion algorithm are developed by using a kernel-based interpolation approach. Our objective is to reconstruct the underlying urban traffic pattern with fine spatial and temporal granularity through processing and integrating data from different sources. The fusion algorithm can work with data collected in different space-time resolution, with different level of accuracy and from different kinds of sensors. The properties and performance of the fusion algorithm is evaluated by using a virtual test bed produced by VISSIM microscopic simulation. The methodology is demonstrated through a real-world application in Central London.

The results show that the proposed algorithm is able to reconstruct accurately the underlying traffic flow pattern on transport network facilities with ordinary data sources on both virtual and real-world test beds. The data sources considered herein include loop detectors, cameras and GPS devices. The proposed data fusion algorithm does not require assumption and calibration of any underlying model. It is easy to implement and compute through advanced technique such as parallel computing.

The presented study is among the first utilizing and integrating heterogeneous urban traffic data from a major city like London. Unlike many other existing studies, the proposed method is data driven and does not require any assumption of underlying model. The formulation of the data fusion algorithm also allows it to be parallelized for large-scale applications. The study contributes to the application of Big Data analytics to infrastructure management.

Background: Geostatistics focuses on spatial or spatiotemporal datasets. Geostatistics was initially developed to generate probability distribution predictions of ore grade in the mining industry; however, it has been successfully applied in diverse scientific disciplines. This technique includes univariate, multivariate, and simulations. Kriging geostatistical methods, simple, ordinary, and universal Kriging, are not multivariate models in the usual statistical function. Notwithstanding, simple, ordinary, and universal kriging techniques utilize random function models that include unlimited random variables while modeling one attribute. The coKriging technique is a multivariate estimation method that simultaneously models two or more attributes defined with the same domains as coregionalization.

Objective: This study investigates the impact of populations on traffic volumes as a variable. The additional variable determines the strength or accuracy obtained when data integration is adopted. In addition, this is to help improve the estimation of annual average daily traffic (AADT).

Methods procedures, process: The investigation adopts the coKriging technique with AADT data from 2009 to 2016 from Montana, Minnesota, and Washington as primary attributes and population as a controlling factor (second variable). CK is implemented for this study after reviewing the literature and work completed by comparing it with other geostatistical methods.

Results, observations, and conclusions: The Investigation employed two variables. The data integration methods employed in CK yield more reliable models because their strength is drawn from multiple variables. The cross-validation results of the model types explored with the CK technique successfully evaluate the interpolation technique's performance and help select optimal models for each state. The results from Montana and Minnesota models accurately represent the states' traffic and population density. The Washington model had a few exceptions. However, the secondary attribute helped yield an accurate interpretation. Consequently, the impact of tourism, shopping, recreation centers, and possible transiting patterns throughout the state is worth exploring.

Many transport and logistics companies nowadays use raw vehicle GPS data for travel time prediction. However, they face difficult challenges in terms of the costs of information storage, as well as the quality of the prediction. This paper aims to systematically investigate various meta-data (features) that require significantly less storage space but provide sufficient information for high-quality travel time predictions.

The paper systematically studied the combinatorial effects of features and different model fitting strategies with two popular decision tree ensemble methods for travel time prediction, namely, random forests and gradient boosting regression trees. First, the investigation was conducted using pseudo travel time data that were generated using a pseudo travel time sampling algorithm, which allows generating travel time data using different noise processes so that the prediction performance under different travel conditions and noise characteristics can be studied systematically. The results and findings were then further compared and evaluated through a real-life case.

The paper provides empirical insights and guidelines about how raw GPS data can be reduced into a small-sized feature vector for the purposes of vehicle travel time prediction. It suggests that, add travel time observations from the previous departure time intervals are beneficial to the prediction, particularly when there is no other types of real-time information (e.g. traffic flow, speed) are available. It was also found that modular model fitting does not improve the quality of the prediction in all experimental settings used in this paper.

The findings are primarily based on empirical studies on limited real-life data instances, and the results may lack generalisabilities. Therefore, the researchers are encouraged to test them further in more real-life data instances.

The paper includes implications and guidelines for the development of efficient GPS data storage and high-quality travel time prediction under different types of travel conditions.

This paper systematically studies the combinatorial feature effects for tree-ensemble-based travel time prediction approaches.

Purpose – Freeway networks are designed to higher standards and are safer infrastructures as compared to other road types, if properly designed. On the other hand, these facilities are driven at very high speeds and therefore speed and design consistency are essential for achieving safe infrastructure designs. This chapter describes the criteria for speed and design consistency and looks at new tools and criteria for improving freeway safety in new and in existing infrastructures.

Methodology – This chapter describes the criteria to evaluate if there are speed, design and human factors inconsistencies, as well as potential solutions for tackling local deficiencies and speeding issues. As one of the critical issues in freeway safety is represented by run-off-road crashes, a specific section in the chapter is devoted to newly developed design and assessment tools for improving roadside safety. The potential implications of Intelligent Transportation System (ITS) technologies on freeways design and management are also presented.

Findings and Social Implications – The important crash reduction trends observed in the decade 2001–2010 are now slowing down and new actions are required to be coupled with more traditional design checks. The full implementation of cooperative ITS systems is expected to have a very important impact on road safety, but in the short term several safety improvements can be realised: section speed enforcement techniques and high-friction wearing courses have been proven to be extremely effective, as have perceptual measures accounting for human factors principles.

The successful and commercial use of self-driving/driverless/unmanned/automated car will make human life easier. The paper aims to discuss this issue.

This paper reviews the key technology of a self-driving car. In this paper, the four key technologies in self-driving car, namely, car navigation system, path planning, environment perception and car control, are addressed and surveyed. The main research institutions and groups in different countries are summarized. Finally, the debates of self-driving car are discussed and the development trend of self-driving car is predicted.

This paper analyzes the key technology of self-driving car and illuminates the state-of-art of the self-driving car.

The main research contents and key technology have been introduced. The research progress as well as the research institution has been summarized.

The purpose of this study is to regard winter heating as a quasi-natural experiment to identify the possible causal effects of winter heating on population mobility. However, there are scant research studies examining the effect of atmospheric quality on population mobility. There also exists some relevant research studies on the relationship between population mobility and environmental degradation (Lu et al., 2018; Reis et al., 2018; Shen et al., 2018), and these studies exist still some deficiencies.

The notorious atmospheric quality problems caused by coal-fired heating in winter of northern China have an aroused widespread concern. However, the quantitative study on the effects on population mobility of winter heating is still rare. In this study, the authors regard the winter heating as a quasi-natural experiment, based on the of daily panel data of 58 cities of Tencent location Big Data in China from August 13 to December 30 in 2016 and August 16 to December 30 in 2017, and examine the impacts of winter heating on population mobility by utilizing a regression discontinuity method.

The findings are as follows, in general, winter heating significantly aggravates regional population mobility, but the impacts on population mobility among different cities are heterogeneous. Specifically, the effects of winter heating on population mobility is greater for cities with relatively good air quality, and the effects is also more obvious for big and medium-sized cities than that in small cities. In addition, different robustness tests, including continuity test, different bandwidth tests and alternative empirical model, are adopted to ensure the reliability of the conclusion. Finally, the authors put forward corresponding policy suggestions from the three dimensions of government, enterprises and residents.

First, regarding winter heating as a quasi-natural experiment, a regression discontinuity design method is introduced to investigate the relationship between winter heating and population mobility, which is helpful to avoid the estimation error caused by endogeneity. Second, the authors use the passenger travel “big data” based on the website of Tencent Location Big Data, which can effectively capture the daily characteristics of China's population mobility. Third, this study discusses the population mobility from the perspective of winter heating and researches population mobility before and after winter heating, which is helpful in enriching the research on population mobility.