Non-recurrent traffic congestion detection on heterogeneous urban road networks

This paper proposes two novel methods for non-recurrent congestion (NRC) event detection on heterogeneous urban road networks based on link journey time (LJT) estimates. Heterogeneity exists on urban road networks in two main aspects: variation in link lengths and data quality. The proposed NRC detection methods are referred to as percentile-based NRC detection and space–time scan statistics (STSS) based NRC detection. Both of these methods capture the heterogeneity of an urban road network by modelling the LJTs with a lognormal distribution. Empirical analyses are conducted on London's urban road network consisting of 424 links for the 20 weekdays of October 2010. Various parameter settings are tested for both of the methods, and the results favour STSS-based NRC detection method over the percentile-based NRC detection method. Link-based analyses demonstrate the effectiveness of the proposed methods in capturing the heterogeneity of the analysed road network

Spatio-temporal Clustering for Non-Recurrent Traffic Congestion Detection on Urban Road Networks

Non-Recurrent Congestion events (NRCs) frustrate commuters, companies and traffic operators because they cause unexpected delays. Most existing studies consider NRCs to be an outcome of incidents on motorways. The differences between motorways and urban road networks, and the fact that incidents are not the only cause of NRCs, limit the usefulness of existing automatic incident detection methods for identifying NRCs on an urban road network. This thesis contributes to the literature by developing an NRC detection methodology to support the accurate detection of NRCs on large urban road networks. To achieve this, substantially high Link Journey Time estimates (LJTs) on adjacent links that occur at the same time are clustered. Substantially high LJTs are defined in two different ways: (i) those LJTs that are greater than a threshold, (ii) those LJTs that belong to a statistically significant Space-Time Region (STR). These two different ways of defining the term ‘substantially high LJT’ lead to different NRC detection methods. To evaluate these methods, two novel criteria are proposed. The first criterion, high-confidence episodes, assesses to what extent substantially high LJTs that last for a minimum duration are detected. The second criterion, the Localisation Index, assesses to what extent detected NRCs could be related to incidents. The proposed NRC detection methodology is tested for London’s urban road network, which consists of 424 links. Different levels of travel demand are analysed in order to establish a complete understanding of the developed methodology. Optimum parameter settings of the two proposed NRC detection methods are determined by sensitivity analysis. Related to the first method, LJTs that are at least 40% higher than their expected values are found to maintain the best balance between the proposed evaluation criteria for detecting NRCs. Related to the second method, it is found that constructing STRs by considering temporal adjacencies rather than spatial adjacencies improves the performance of the method. These findings are applied in real life situations to demonstrate the advantages and limitations of the proposed NRC detection methods. Traffic operation centres could readily start using the proposed NRC detection methodology. In this way, traffic operators could be able to quantify the impact of incidents and develop effective NRC reduction strategies

Spatio-temporal clustering for non-recurrent traffic congestion detection on urban road networks

Non-Recurrent Congestion events (NRCs) frustrate commuters, companies and traffic operators because they cause unexpected delays. Most existing studies consider NRCs to be an outcome of incidents on motorways. The differences between motorways and urban road networks, and the fact that incidents are not the only cause of NRCs, limit the usefulness of existing automatic incident detection methods for identifying NRCs on urban road networks. In this paper we propose an NRC detection methodology to support the accurate detection of NRCs on large urban road networks. To achieve this, substantially high Link Journey Time estimates (LJTs) on adjacent links that occur at the same time are clustered. Substantially high LJTs are defined as those LJTs that are greater than a threshold. The threshold is calculated by multiplying the expected LJTs with a congestion factor. To evaluate the effectiveness of the proposed NRC detection method, we propose two novel criteria. The first criterion, high-confidence episodes, assesses to what extent substantially high LJTs that last for a minimum duration are detected. The second criterion, the Localisation Index, assesses to what extent detected NRCs could be associated with incidents. The proposed NRC detection methodology is tested for London's urban road network. The optimum value of the congestion factor is determined by sensitivity analysis by using a Weighted Product Model (WPM). It is found out those LJTs that are at least 40% higher than their expected values should belong to an NRC; as such NRCs are found to maintain the best balance between the proposed evaluation criteria

The rank reversal problem in multi-criteria decision making : a literature review

Despite the importance of multicriteria decision-making (MCDM) techniques for constructing effective decision models, there are many criticisms due to the occurrence of a problem called rank reversal. Nevertheless, there is a lack of a systematic literature review on this important subject which involves different methods. This study reviews the pertinent literature on rank reversal, based on 130 related articles published from 1980 to 2015 in international journals, which were gathered and analyzed according to the following perspectives: multicriteria technique, year and journal in which the papers were published, co-authorship network, rank reversal types, and research goal. Thus our survey provides recommendations for future research, besides useful information and knowledge regarding rank reversal in the MCDM field

How Travel Demand Affects Detection Of Non-Recurrent Traffic Congestion On Urban Road Networks

Occurrence of non-recurrent traffic congestion hinders the economic activity of a city, as travellers could miss appointments or be late for work or important meetings. Similarly, for shippers, unexpected delays may disrupt just-in-time delivery and manufacturing processes, which could lose them payment. Consequently, research on non-recurrent congestion detection on urban road networks has recently gained attention. By analysing large amounts of traffic data collected on a daily basis, traffic operation centres can improve their methods to detect non-recurrent congestion rapidly and then revise their existing plans to mitigate its effects. Space-time clusters of high link journey time estimates correspond to non-recurrent congestion events. Existing research, however, has not considered the effect of travel demand on the effectiveness of non-recurrent congestion detection methods. Therefore, this paper investigates how travel demand affects detection of non-recurrent traffic congestion detection on urban road networks. Travel demand has been classified into three categories as low, normal and high. The experiments are carried out on London's urban road network, and the results demonstrate the necessity to adjust the relative importance of the component evaluation criteria depending on the travel demand level.WoSScopu

A Review on Citizen Science (Citsci) Applications for Disaster Management

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Educational material development on mobile spatial data collection using open source geospatial technologies

Mobile spatial data collection is one of the major tasks carried out under the United Nations (UN) to quantify targets of various Sustainable Development Goals (SDGs). Even though there are readily available proprietary solutions to ease mobile spatial data collection, there is often limited educational resources that rely on Free and Open Source Software for Geospatial (FOSS4G). The ongoing efforts of UN OSGeo Committee are to satisfy the requirements of UN operations in terms of identifying and developing open source geospatial software and services. The aim of this paper is to describe and discuss the educational material (tutorial) that is prepared to address the 2019 UN OSGeo Committee Educational Challenge entitled ‘Open geospatial data and software for UN SDG 16, Peace justice and open institution’. The educational material utilised various technologies, including QGIS, QField, PostgreSQL/PostGIS and NodeJS, by relying on a simple mobile spatial data collection scenario, which is collecting information about trees. The scenario serves as an analogy for many different UN operations that might fall under SDG 16, such as interviews with a victim or a vulnerable person or estimating to what extent decision-making is inclusive and responsive. The developed educational material addresses some of the practical requirements of mobile spatial data collection, including handling multiple users, offline data collection, attaching photos to recorded events, editing of spatial features in the field and displaying the collected data on the internet using Heroku. Even though the educational material intends to be self-explanatory, quantitative and qualitative measures are needed to evaluate its efficacy. Therefore, a group of geomatics engineering students were trained with the educational material and their feedbacks were collected through a satisfaction questionnaire as well as the completion time of the tutorial. The outcomes of this paper will be useful in terms of guiding researchers and practitioners who develop online educational material, especially in the geospatial domain.https://www.isprs.org/publications/archives.aspxpm2021Geography, Geoinformatics and Meteorolog

Predicting the Spatial Impact of Planned Special Events

While it is widely acknowledged that Planned Special Events (PSEs), such as concerts, soccer games, etc., have a strong impact on road traffic, very few studies have quantitatively investigated this phenomenon. In this paper we present the preliminary results of a technique to quantify the impact of PSEs on traffic around the venue of the events. In particular, our goal was to automatically identify all those road segments around a venue that show a different traffic behavior on event days than on non-event days. To this aim, we defined a specific pipeline, including a K-Nearest Neighbor classifier, trained on traffic data of event and non-event days for each road, using the Dynamic Time Warp (DTW) as distance metric. The proposed solutions has been empirically evaluated on four PSE venues in Germany. Two of them hosted only soccer matches of the German First League, while the other two had mixed types of PSEs, including sport, concerts and other categories of events. Results are very positive for the soccer stadiums, while more research is needed for the venues hosting mixed types of PSEs