Geospatial dashboards for intelligent multimodal traffic management

This paper presents the current status and future outlook of Traffic Management as a Service (TMaaS). TMaaS is an innovative web platform that provides a cloud-based vendor-neutral multimodal traffic management solution for small and medium-sized cities. Urban mobility data from several stakeholders and public service providers is integrated and visualized in a clean, intuitive and customizable interface for traffic operators and citizens

TMaaS, a new cloud-based, vendor-neutral multimodal traffic management solution

Traffic Management as a Service (TMaaS) is an innovative collaboration between eight public and private partner organisations, including the City of Ghent (main urban authority). This consortium has spent the past three years researching and developing a web application that provides a cloud-based, vendor-neutral multimodal traffic management solution for small and medium-sized cities. Urban mobility data from several stakeholders and public service providers are integrated and visualized in a clean, intuitive and customizable interface for traffic operators and citizens. TMaaS is a European project co-funded by the Urban Innovative Actions initiativ

Error sources in the analysis of crowdsourced spatial tracking data

Governments are increasingly interested in the use of crowdsourced spatial tracking data to gain information on the travel behaviour of their citizens. To improve the reliability of reporting in such mobility studies, this paper systematically analyses the propagation of errors from low level operations to high level indicators, such as the modal split and travelled distances. We find that most existing metrics in literature are insufficient to fully quantify this evolution of data quality. The propagation channels are presented schematically and a new approach to quantify the spatial data quality at the end of each processing stage is proposed. This procedure, within the context of Smart Cities, ensures that the data analytics and resulting changes in policy are sufficiently substantiated by credible and reliable information

Het meten van wachttijden voor fietsers op basis van floating bike data

De fietskwaliteit van lichtengeregelde kruispunten wordt veelal beoordeeld op basis van de maximale of gemiddelde wachttijd voor fietsers. Nochtans bestaat hierover zelden gemeten data en wordt gebruikt gemaakt van verwachte waarden op basis van de eigenschappen van de lichtenregeling (cyclustijd, groentijd). Deze paper bepaalt de wachttijden voor fietsers op basis van GPS-data van fietsverplaatsingen (floating bike data). De opgelopen verliestijd tijdens de passage over het kruispunt wordt berekend door het vergelijken van de GPS-gegevens op een punt voor het kruispunt met een punt net na het kruispunt. In eerste instantie wordt de stabiliteit van de resultaten bekeken in de mate dat het eerste GPS-punt verder van of dichter bij het kruispunt wordt gekozen. Hieruit blijkt dat een GPS- punt (te) dicht voor het kruispunt leidt tot een onderschatting van de verliestijd, mogelijk omdat verliestijd bij het naderen van het kruispunt niet mee bemeten wordt. Bij het naderen van een rood verkeerslicht vertragen fietsers immers in de regel, om een volledige stop voor het kruispunt te vermijden. Tegelijk mag het GPS-punt echter ook niet te ver voor het kruispunt gekozen worden, omdat men dan riskeert om andere vormen van vertraging mee te bemeten, die niet aan het lichtengeregeld kruispunt te wijten zijn. Daarnaast werden de berekende wachttijden vergeleken met de theoretisch verwachte waarden op basis van de lichtenregeling. Voor de meeste beschouwde kruispunten levert dit een goede overeenkomst op. Voor twee kruispunten liggen de berekende waarden echter beduidend hoger dan de verwachte waarden. Een mogelijke verklaring hiervoor is dat niet voldaan is aan de aanname dat fietsers gelijkmatig verdeeld aankomen op het kruispunt, bijvoorbeeld omdat ze geclusterd zijn door een stroomopwaarts gelegen lichtengeregeld kruispunt. Dit toont echter aan dat het gebruik van geobserveerde wachttijdgegevens wel degelijk een zinvolle verfijning is ten opzichte van de courant gebruikte verwachte wachttijd. Het gebruik van gemeten waarden, zoals hier berekend op basis van floating bike data, is dus wel degelijk een zinvolle verfijning van de huidige benadering en kan worden aangewend om het comfort van fietsers te verhogen

Measuring delays for bicycles at signalized intersections using smartphone GPS tracking data

The article describes an application of global positioning system (GPS) tracking data (floating bike data) for measuring delays for cyclists at signalized intersections. For selected intersections, we used trip data collected by smartphone tracking to calculate the average delay for cyclists by interpolation between GPS locations before and after the intersection. The outcomes were proven to be stable for different strategies in selecting the GPS locations used for calculation, although GPS locations too close to the intersection tended to lead to an underestimation of the delay. Therefore, the sample frequency of the GPS tracking data is an important parameter to ensure that suitable GPS locations are available before and after the intersection. The calculated delays are realistic values, compared to the theoretically expected values, which are often applied because of the lack of observed data. For some of the analyzed intersections, however, the calculated delays lay outside of the expected range, possibly because the statistics assumed a random arrival rate of cyclists. This condition may not be met when, for example, bicycles arrive in platoons because of an upstream intersection. This justifies that GPS-based delays can form a valuable addition to the theoretically expected values

Density-based spatial clustering and ordering points approach for characterizations of tourist behaviour

Knowledge about the spots where tourist activity is undertaken, including which segments from the tourist market visit them, is valuable information for tourist service managers. Nowadays, crowdsourced smartphones applications are used as part of tourist surveys looking for knowledge about the tourist in all phases of their journey. However, the representativeness of this type of source, or how to validate the outcomes, are part of the issues that still need to be solved. In this research, a method to discover hotspots using clustering techniques and give to these hotspots a data-driven interpretation is proposed. The representativeness of the dataset and the validation of the results against existing statistics is assessed. The method was evaluated using 124,725 trips, which have been gathered by 1505 devices. The results show that the proposed approach successfully detects hotspots related with the most common activities developed by overnight tourists and repeat visitors in the region under study

Error sources in the analysis of crowdsourced spatial tracking data

Governments are increasingly interested in the use of crowdsourced spatial tracking data to gain information on the travel behaviour of their citizens. This approach requires that the accuracy and the reliability of the data and transformation processes are clearly characterized. To improve the reliability of reporting in such mobility studieswe systematically analyse the propagation of errors from low level operations to high level indicators, such as the modal split and travelled distances. Studies have shown that errors that occur in early stages of the data processing can have drastic consequences on the accuracy of later stages. We find that most existing metrics in literature are insufficient to fully quantify this evolution of data quality. The propagation channels are presented schematically and a new approach to quantify the spatial data quality at the end of each processing stage is proposed. This procedure, within the context of Smart Cities, ensures that the data analytics and resulting changes in policy are sufficiently substantiated by credible and reliable information

Repurposing existing traffic data sources for COVID-19 crisis management

Mobility behavior was impacted severely by the COVID-19 health crisis. To understand the changing situation, crisis managers need access to credible and timely data. In this paper, we look at the potential of traffic management data for crisis management. We list the different categories and types of traffic data sources and provide an overview of how policymakers, research institutions and private companies can repurpose their data to monitor the effect of the crisis and the accompanying lockdown measures on mobility behavior. Finally, we illustrate this through two use cases in the Belgian city of Ghent, and conclude that existing information from connected infrastructure in smart cities can be quickly repurposed with minimal effort

Service response time estimation in crowdsourced processing chain

Self-organized platforms to support Citizen Observatories need to build data processing chains based on the study’s goal where the non-functional (e.g., response time) requirements are as important as the functional requirements. In this research, a method to estimate the average response time of data services commonly found in crowdsourced data processing chains by using basic statistics from the data distribution of previous campaigns is proposed. The method is evaluated using 18.512 registers of map-matched trip segments collected in a citizen mobility campaign gathered by 310 devices. Results show that the proposed method report an error rate between 5.55% and 12.55% when the transport mode is not considered, and between 5.12% and 9.41% when the transport mode is used