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JournalISSN: 1866-8887

European Transport Research Review 

SpringerOpen
About: European Transport Research Review is an academic journal published by SpringerOpen. The journal publishes majorly in the area(s): Poison control & Public transport. It has an ISSN identifier of 1866-8887. It is also open access. Over the lifetime, 608 publications have been published receiving 11350 citations. The journal is also known as: An open access journal.


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Journal ArticleDOI
TL;DR: The prediction scheme proposed for traffic flow prediction could be considered in situations where database is a major constraint during model development using ARIMA, which is acceptable in most of the ITS applications.
Abstract: Accurate prediction of traffic flow is an integral component in most of the Intelligent Transportation Systems (ITS) applications. The data driven approach using Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) models reported in most studies demands sound database for model building. Hence, the applicability of these models remains a question in places where the data availability could be an issue. The present study tries to overcome the above issue by proposing a prediction scheme using Seasonal ARIMA (SARIMA) model for short term prediction of traffic flow using only limited input data. A 3-lane arterial roadway in Chennai, India was selected as the study stretch and limited flow data from only three consecutive days was used for the model development using SARIMA. After necessary differencing to make the input time series a stationary one, the autocorrelation function (ACF) and partial autocorrelation function (PACF) were plotted to identify the suitable order of the SARIMA model. The model parameters were found using maximum likelihood method in R. The developed model was validated by performing 24 hrs. ahead forecast and the predicted flows were compared with the actual flow values. A comparison of the proposed model with historic average and naive method was also attempted. The effect of increase in sample size of input data on prediction results was studied. Short term prediction of traffic flow during morning and evening peak periods was also attempted using both historic and real time data. The mean absolute percentage error (MAPE) between actual and predicted flow was found to be in the range of 4–10, which is acceptable in most of the ITS applications. The prediction scheme proposed in this study for traffic flow prediction could be considered in situations where database is a major constraint during model development using ARIMA.

473 citations

Journal ArticleDOI
TL;DR: In this paper, the authors tackle the theme of evaluating dynamic load increases that the vehicle transfers to the road pavement, due to the generation of vibration produced by surface irregularities, and show how this dynamic overload may be predetermined as a function of the pavements surface degradation.
Abstract: The paper tackles the theme of evaluating dynamic load increases that the vehicle transfers to the road pavement, due to the generation of vibration produced by surface irregularities. The study starts from the generation, according to the ISO 8608 Standard, of different road roughness profiles characterized by different damage levels. In particular, the first four classes provided by ISO 8608 were considered. Subsequently, the force exchanged between the pavement and three typologies of vehicles (car, bus and truck) has been assessed by implementing, in Matlab®, the QCM (Quarter Car Model) characterized by a quarter vehicle mass and variable speed from 20 to 100 km/h. The analysis allows determining the amount of dynamic overload that causes the vibrational stress. The paper shows how this dynamic overload may be predetermined as a function of the pavements surface degradation. This is a useful reference for the purposes of designing and maintaining road pavements.

197 citations

Journal ArticleDOI
TL;DR: In this article, the authors developed a suitable and comprehensive definition for crowd logistics and identified which factors determine the sustainability potential of CL and indicated whether the identified characteristics affect the economy, society and/or environment.
Abstract: Passenger car occupancy has been falling for years. Partly empty vehicles on our road networks decrease passenger transport sustainability but also contain an opportunity for freight transport. Within Crowd logistics (CL), delivery operations are carried out by using passengers’ excess capacity on journeys that are already taking place, resulting in economic, social and environmental benefits. Existing CL initiatives show, however, that there are important differences between concepts in terms of sustainability. The research aims to develop a suitable and comprehensive definition for CL and identify which factors determine the sustainability potential of CL. We systematically analysed a set of 42 papers and interviewed 11 logistics practitioners in order to capture the state of practice. Following the literature and interviews, we firstly define CL as ‘an information connectivity enabled marketplace concept that matches supply and demand for logistics services with an undefined and external crowd that has free capacity with regards to time and/or space, participates on a voluntary basis and is compensated accordingly’. Secondly, we identify a set of 18 characteristics that can describe the variety of CL concepts. Thirdly, we indicate whether the identified characteristics affect the economy, society and/or environment. The research shows that all characteristics influence economic sustainability while 11 characteristics also affect social and/or environmental sustainability. Our research helps local policy-makers to adapt laws and regulations to the sharing economy developments and provides insight for businesses which CL concept fits their company’s corporate social responsibility strategy.

194 citations

Journal ArticleDOI
Huansheng Song1, Liang Haoxiang1, Huaiyu Li1, Dai Zhe1, Yun Xu1 
TL;DR: A new high definition highway vehicle dataset with a total of 57,290 annotated instances in 11,129 images is published in this study, which provides the complete data foundation for vehicle detection based on deep learning.
Abstract: Intelligent vehicle detection and counting are becoming increasingly important in the field of highway management. However, due to the different sizes of vehicles, their detection remains a challenge that directly affects the accuracy of vehicle counts. To address this issue, this paper proposes a vision-based vehicle detection and counting system. A new high definition highway vehicle dataset with a total of 57,290 annotated instances in 11,129 images is published in this study. Compared with the existing public datasets, the proposed dataset contains annotated tiny objects in the image, which provides the complete data foundation for vehicle detection based on deep learning. In the proposed vehicle detection and counting system, the highway road surface in the image is first extracted and divided into a remote area and a proximal area by a newly proposed segmentation method; the method is crucial for improving vehicle detection. Then, the above two areas are placed into the YOLOv3 network to detect the type and location of the vehicle. Finally, the vehicle trajectories are obtained by the ORB algorithm, which can be used to judge the driving direction of the vehicle and obtain the number of different vehicles. Several highway surveillance videos based on different scenes are used to verify the proposed methods. The experimental results verify that using the proposed segmentation method can provide higher detection accuracy, especially for the detection of small vehicle objects. Moreover, the novel strategy described in this article performs notably well in judging driving direction and counting vehicles. This paper has general practical significance for the management and control of highway scenes.

186 citations

Journal ArticleDOI
TL;DR: The Super Light Car (SLC) project is one of the most important research projects in the European Community for automotive lightweight construction with a multi-material approach as mentioned in this paper, and the motivation and objective for a front structure designed with a light multilayer-mix.
Abstract: The Super Light Car (SLC) project is one of the most important research projects in the European Community for automotive lightweight construction with a multi-material approach. The paper shows the motivation and objective for a front structure designed with a light multi-material-mix.

170 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
202320
202290
202160
202067
201951
201863