Sie können Operatoren mit Ihrer Suchanfrage kombinieren, um diese noch präziser einzugrenzen. Klicken Sie auf den Suchoperator, um eine Erklärung seiner Funktionsweise anzuzeigen.
Findet Dokumente, in denen beide Begriffe in beliebiger Reihenfolge innerhalb von maximal n Worten zueinander stehen. Empfehlung: Wählen Sie zwischen 15 und 30 als maximale Wortanzahl (z.B. NEAR(hybrid, antrieb, 20)).
Findet Dokumente, in denen der Begriff in Wortvarianten vorkommt, wobei diese VOR, HINTER oder VOR und HINTER dem Suchbegriff anschließen können (z.B., leichtbau*, *leichtbau, *leichtbau*).
Dieses Kapitel befasst sich mit der Bewertung eines intelligenten LKW-Parkdienstes (ITPS) entlang des Autobahnkorridors E17 in Flandern, Belgien. Die Studie behandelt drei zentrale Forschungsfragen: die beste Technologie zur Messung der Parkplatzbelegung, die Auswirkungen der Bereitstellung von Belegungsinformationen für Lastwagenfahrer und die Erfahrungen der Nutzer mit den bereitgestellten Informationen. Die Bewertung umfasste technische Analysen, Folgenabschätzungen, Umfragen zur Nutzerakzeptanz und Stakeholder-Interviews. Zu den wichtigsten Ergebnissen zählen die Effektivität verschiedener Erkennungsmethoden, der Einfluss variabler Message Signs (VMS) und einer App auf das Parkverhalten sowie die allgemeine Zufriedenheit der Nutzer mit den bereitgestellten Informationen. Das Kapitel enthält auch Empfehlungen zur Verbesserung der Lkw-Parkinfrastruktur und betont die Notwendigkeit eines globaleren Ansatzes, der bessere rechtliche Rahmenbedingungen, Durchsetzung und zusätzliche Parkmöglichkeiten umfasst. Die Studie unterstreicht die Bedeutung von Datengenauigkeit, benutzerfreundlicher Informationsbereitstellung und der Zusammenarbeit von Stakeholdern bei der Bewältigung von Herausforderungen im Bereich der Lkw-Parkplätze.
KI-Generiert
Diese Zusammenfassung des Fachinhalts wurde mit Hilfe von KI generiert.
Abstract
The Flemish government launched two related projects in Belgium between 2020 and 2023. First, they rolled out an Intelligent Truck Parking Service (ITPS) along a part of the E17 motorway. Then we supported them to develop a vision on truck parking. The ITPS consisted of an app for truck drivers, combined with information on variable message signs. We evaluated the ITPS so as to provide answers to the following research questions: (i) which technology best measures parking occupancy? (ii) what is the impact of providing parking occupancy information to the truck driver? (iii) how do users deal with the information (user experience)? In order to evaluate the ITPS, we performed a technical analysis, an im-pact analysis, a user acceptance analysis, and performed interviews with stakeholders regarding the eco-system. In addition, the analysis of GPS measurements provided insights into used parking locations and occupancies for developing the vision.
1 Background and Research Questions
The Flemish government launched two related projects in Belgium between 2020 and 2023. First, they rolled out an Intelligent Truck Parking Service (ITPS) testing ground along the E17 motorway corridor between the community of Kalken and the French border (see also Fig. 1). Then we supported them to develop a vision on truck parking. After some quality improvements, the ITPS was commissioned with the launch of an app, a dynamic DATEX II flow, a web interface, and the visualisation on VMS boards. The goal of the evaluation of the ITPS was to provide answers to the following three research questions:
RQ1: What is the best technology to measure parking occupancy?
RQ2: What is the impact of providing information to the truck driver on the occupancy of the parking?
RQ3: How do users deal with the information (user experience)?
During our evaluation, we performed a technical analysis, an impact analysis, a user acceptance analysis, and performed interviews with stakeholders regarding the ecosystem. Finally, we analysed GPS measurements to obtain insights into used parking locations and occupancies for developing a vision on truck parking. We will provide details on each of these aspects in the following sections.
Fig. 1.
Geographical overview of the truck parkings along the E17 motorway within the pilot project. (created using QGIS and Microsoft PowerPoint)
A number of detection methods (not the same everywhere) have been made available at the various rest areas and service zones, including (i) loop detectors, (ii) barriers and ticket system, (iii) parking sensors, (iv) DSRC readers, (v) traffic sensors, and (vi) truck OBU data. In order to assess the accuracy of the measurement systems, manual counts were performed (which were also initially used to (re)calibrate the systems). During the baseline measurement, one measurement was performed per car park every day, at a specific time, for two weeks between September 15, 2020 and September 28, 2020. This means that a total of 14 measurements were performed per car park over the measurement period. Four measurement moments are possible per day: morning (between 6 am and 12 pm), afternoon (between 12 pm and 6 pm), evening (between 6 pm and midnight) and night (between midnight and 6 am). For each manual count, we determined the absolute and the percentage error, and the mean absolute percent error (MAPE) in relation to the different measurement methods (in the morning when there are fewer trucks, the absolute error is decisive, at night the percentage error is decisive, and in the afternoon both are important). For each type of count, we also looked at the statistical distribution of the errors, and whether outliers occur.
2.2 Impact Analysis
To measure the impact of the different incentives on the occupancy rate, we organised three successive phases:
Phase 1: this is the baseline measurement (there is no app and the VMS boards are not active)
Phase 2: an app is available to the truck drivers (Truckmeister, on Android and iOS)
Phase 3: the app is available and the VMS boards are active.
For each of these phases, we performed an analysis of the parking occupancies, the impact of the VMS, and the results from the OBU data (Fig. 2).
Fig. 2.
Example data sources used for the analysis of the research questions: parking occupancies per day of the week (upper-left), distribution of parking durations in minutes (upper-right), truck OBU data from trucks (bottom-left), truck OBU data matched on motorway parking terrains (bottom-right). Figures created using Microsoft Excel, Java, and QGIS.
We rolled out a survey after the end of phase 3, containing an extensive questionnaire. The survey could not be conducted in the car parks themselves due to the COVID-19 pandemic and the associated measures. As such, we created it digitally. Because the truck drivers had to be reached on the route, the concession holders of the car parks were approached for this, advertisements were posted in specific Facebook groups of truck drivers (both those of the government and others), via LinkedIn posts, and finally it was also distributed among the truck drivers of a specific local distribution company. In addition, QR codes were also made available, which referred to the survey. The survey was available in Dutch, French, English, and German. The survey was closely monitored week after week, with appropriate actions taken to receive statistically significant feedback and a high response rate. In total we had 256 complete (42%) and 349 (58%) incomplete answers for a total of 605 together.
2.3.2 Surveying Truck Drivers
As a final step in our evaluation, we held interviews with nine relevant stakeholders from the ecosystem: transport sector organisations, private concession holders, public stakeholders, service providers, and stakeholders from policy. Through the interview we wanted to gauge the use, the possibilities, and the findings about services that provide information about the occupancy rate. Each interview included the following three main parts that were surveyed from all stakeholders:
In the first part, we asked about more general aspects of service zones and the extent to which the interviewee or the organisation has knowledge about the services to communicate occupancy rates to truck drivers or others.
In the second part, we mainly asked how the organisation feels about this service.
In a final part, we discussed the specific aspects of this service: to what extent is it effective? What impact can it have on the organisation? How do you see the costs and benefits? Etc.
3 Conclusions
3.1 Research Question 1: Which Technology Best Measures Parking Occupancy?
After analysing parking occupancy measurements, the answer to this question appears to be nuanced, whereby the traffic sensors appear to score less well, barriers score quite well, as do parking sensors (if there is no overcrowding). This answer is based on a detailed technical evaluation of measuring methods for the dynamic occupancy rates of truck parkings.
Anzeige
Analyses of the OBU data showed that most trucks that were already in the car park leave before 7 am (these have mainly Poland, Belgium, Lithuania, the Netherlands, and Romania as countries of registration). Conversely, most trucks stop (for the rest of the day) more at Nazareth and Marke than the other car parks. The bulk of trucks routinely arrive at a car park throughout the day, with a slight dip around 8 am. When it comes to regular car park arrivals, Kalken, Nazareth, and Marke have the highest volumes. In these, the parking peak is earlier in the morning/early afternoon. Finally, we also analysed the lorries’ dwell times in the car parks. There is a large spread here with local maxima. Nevertheless, the majority of trucks appear to be in the car park for only a very short time (less than 10 min), followed by two groups around 35 and 50 min. These two local peaks occur around 10h00 -11h00.
3.2 Research Question 2: What is the Impact of Providing Parking Occupancy Information to the Truck Driver?
To measure the impact of the different incentives on the occupancy rate, we organised three successive phases: a baseline measurement, an app made available to the truck drivers, and both an app and active VMS boards. Compared to the baseline measurement in phase 1, making the app available in phase 2 had a negligible to nonexistent effect on the parking behaviour of truck drivers (due to the very low penetration rate).
It was not possible to uncover a direct relationship between the messages on the VMS signs and parking occupancies. Nevertheless, the VMS signs were the only variable across the three phases, excluding other factors such as seasonal effects, for example, and we could not observe any other measures taken or behavioural changes. Consequently, the activation of the VMS signs in phase 3 probably did have a positive effect, with the indication of the current occupancy rate presumably leading to a better distribution in the car parks. Note that the VMS signs mainly provided information for the car parks at Nazareth, Kruishoutem, and Marke. Only the VMS sign near Kortrijk also gave information for Rekkem.
3.3 Research Question 3: How Do Users Deal with the Information (User Experience)?
We rolled out our own survey after phase 3, which contained an extensive questionnaire. Almost all respondents (239 of 256, 93%) drove on the A10 / E17 towards France, which gave relevant results. There were 164 respondents (69%) who used the information from the VMS. Almost all (89%) found the information offered useful to very useful. Most of the respondents (76%) thought that the information on the VMS was usually correct, and if this was not the case, it turned out that there was no space left while the signs indicated the opposite (just like with the app). In the future, a large proportion of truck drivers (80%) would find an app useful to very useful, and a smaller proportion (20%) rather useless.
4 Recommendations
The issue of truck parking in Flanders has been studied and plays out in three areas:
1.
Truck parking in motorway car parks, especially in service areas during night and weekend rest. This problem occurs on all axes of freight transport on motorways in Flanders. The problem is most pronounced in the Ghent-Brussels-Antwerp triangle and extends along the E17 towards the French border;
2.
Truck parking along regional roads and near residential areas. This often involves truck parking by employees who take the truck home and park it locally. It is more of a local problem where parked trucks take up public space and sometimes cause danger;
3.
Truck parking for loading and unloading where trucks occupy public domain or wait on the street until it is their turn or their time slot to load or unload. The problem here is that trucks sometimes block public roads.
A total of over 5,300 long-term parking events per day are measured in Flanders. Parking on the secondary road network is often linked to the function of the location. Areas attracting freight traffic (industrial zones, airports, and ports) have more trucks parked along the road. Of the total number of trucks parked, about one-fifth are parked in unofficial car parks.
Our study had a very broad approach, in which a number of aspects were highlighted in great detail. To answer the three research questions, various analyses were performed, experiments were set up, and groups of users were questioned. There are consequently a number of comments with regard to the implementation of the study and the interpretation of the results.
Before such a study like this can be started, the technical systems to be considered must already be operating under good conditions. This was not always the case, which meant that certain analyses had to be repeated several times.
Various problems occurred during the study, including detection methods that were set over time with different parameters, problems with technical installations, missing measurements, manual counts that were partly incorrect, etc.
Further research could be useful, given that a significant number of manual counts are available. This would then make it possible to determine whether data is best fused or not, or recalibrated regularly, and to what extent raw data is suitable for achieving a good fusion. A larger amount of data and a greater mutual comparability of the various locations are strong pluses. Another caveat to the impact analyses is that more data would in any case lead to more stable results.
A comment on the impact analyses is that more data would lead to more stable results anyway. Initially, a longer baseline measurement is recommended (at least four weeks). On top of that, the app penetration rate needs to be significantly higher if we want to measure any impact from that. Finally, it is also true that the spatial scope of the study is limited on two levels: on the one hand, we do not know what happens beyond the border with France (after Rekkem), and on the other hand, parking could also occur on breakdown lanes, and possibly even on (car parks on) the underlying road network.
It is not clear whether VMS or the app can guarantee a better service: VMS appears to be necessary if one gets closer to the car parks. An app allows for more options and can, if necessary, be more complete. The reluctance to an app is more about the use of a mobile phone and road safety. If this service is integrated into the truck itself, the reluctance is less. Providing the service is mainly placed in the hands of the government by some stakeholders, one expects that the government will outline the guidelines and assume responsibility for both the detection facilities and the provision of data.
The problem of overcrowded car parks and the need for a better distribution of use is almost unanimously endorsed. Service provision, as in the pilot project, will certainly make its contribution but should be seen as part of a more global approach: better legislative framework, enforcement, more parking facilities including (private) ones not along motorways, etc. A parking policy/vision therefore urges both regional and European. The main stakeholders seen are the government, parking operators (concessions/private/public), service providers and truck manufacturers. The provision of the service is mainly put in the government's shoes. People expect the government to set the outline and take on both the detection provisions and the provision of data.
There is certainly the will of organisations to contribute to tackle the problem. How far one wishes to go as an organisation to facilitate this service is closely related to the business potential of the service and what role one should assume as a stakeholder (facilitator, data provider, etc.). Minimum information that should be provided is indicating which car park is full or free and the travel time to the relevant (free) car park. Preference is given to using figures/icons and as little text as possible.
Acknowledgements
The projects referenced in this paper have received funding from the European Union’s Connected European Facilities (CEF) programme under grant agreement No 2014-BE-TM-0694-S, the Flemish government, Administration of Roads and Traffic, contract No TDV.064.EVCITS_009, and the Flemish Department of Mobility and Public Works.
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
