Abstract

The research team is currently working on defining a suitable path towards the design and implementation of a Living Lab for developing, testing and demonstrating innovations in sustainable transport operations. There are several examples that focus on transport and mobility, either addressing individual or freight transport. The combination of both topics is seen as a unique chance to find new ways for a sustainable transport system and mobility behavior. The region of Upper Austria is used as a research case in order to demonstrate results and findings of an applied research project, called “Mobility Lab Upper Austria.” Document type: Article

Abstract

Recently different studies of green transport have become interesting for policy makers, car manufacturers, customers and energy suppliers. Many stakeholders from the public and private sectors are investing a lot of effort to identify consumer behaviour for future improvements in development of green products and effective strategies, which could accelerate the transition to sustainable future. This paper presents the effects of electric vehicle promotional policies and customer preferences about alternative fuel vehicles. This study has shown that the electric vehicle promotional policies adopted in Slovenia have been unsuccessful, as the share of first-time registered electric vehicles in 2013 was below 1%. For different segments of people whose opinions about low emission vehicles differ, different measures must be adopted. When designing promotional policies focus must be on the most relevant factors such as the total vehicle price and fuel economy.

Abstract

Erosive rainfall varies pronouncedly in time and space. Severe events are often restricted to a few square kilometers. Radar rain data with high spatiotemporal resolution enable this pattern of erosivity to be portrayed with high detail. We used radar data with a spatial resolution of 1 km"span class="inline-formula"""sup"2"/sup""/span" over 452 503 km"span class="inline-formula"""sup"2"/sup""/span" to derive a new erosivity map for Germany and to analyze the seasonal distribution of erosivity. The expected long-term regional pattern was extracted from the scattered pattern of events by several steps of smoothing. This included averaging erosivity from 2001 to 2017 and smoothing in time and space. The pattern of the resulting map was predominantly shaped by orography. It generally agrees well with the erosivity map currently used in Germany (Sauerborn map), which is based on regressions using rain gauge data (mainly from the 1960s to 1980s). In some regions the patterns of both maps deviate because the regressions of the Sauerborn map were weak. Most importantly, the new map shows that erosivity is about 66 % larger than in the Sauerborn map. This increase in erosivity was confirmed by long-term data from rain gauge stations that were used for the Sauerborn map and which are still in operation. The change was thus not caused by using a different methodology but by climate change since the 1970s. Furthermore, the seasonal distribution of erosivity shows a slight shift towards the winter period when soil cover by plants is usually poor. This shift in addition to the increase in erosivity may have caused an increase in erosion for many crops. For example, predicted soil erosion for winter wheat is now about 4 times larger than in the 1970s. These highly resolved topical erosivity data will thus have definite consequences for agricultural advisory services, landscape planning and even political decisions. Document type: Article

Abstract

This paper presents a method to find the optimal configuration for an electric vehicle energy storage system using a cascaded H-bridge (CHB) inverter. CHB multilevel inverters enable a better utilization of the battery pack, because cells/modules with manufacturing tolerances in terms of capacity can be selectively discharged instead of being passively balanced by discharging them over resistors. The balancing algorithms have been investigated in many studies for the CHB topology. However, it has not yet been investigated to which extend a conventional pack can be modularized in a CHB configuration. Therefore, this paper explores different configurations by simulating different switch models, switch configurations, and number of levels for a CHB inverter along with a reference load model to find the optimal design of the system. The configuration is also considered from an economically point of view, as the most efficient solution might not be cost-effective to be installed in a common production vehicle. It is found that four modules per phase give the best compromise between efficiency and costs. Paralleling smaller switches should be preferred over the usage of fewer, larger switches. Moreover, selecting specific existing components results in higher savings compared to theoretical optimal components. Document type: Article

Abstract

Bhattacharya, D., Painho, M., Mishra, S., & Gupta, A. (2017). Mobile traffic alert and tourist route guidance system design using geospatial data. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (Vol. 42, pp. 11-18). (ISPRS Archives; Vol. 42, No. 4W3). https://doi.org/10.5194/isprs-archives-XLII-4-W3-11-2017 The present study describes an integrated system for traffic data collection and alert warning. Geographical information based decision making related to traffic destinations and routes is proposed through the design. The system includes a geospatial database having profile relating to a user of a mobile device. The processing and understanding of scanned maps, other digital data input leads to route guidance. The system includes a server configured to receive traffic information relating to a route and location information relating to the mobile device. Server is configured to send a traffic alert to the mobile device when the traffic information and the location information indicate that the mobile device is traveling toward traffic congestion. Proposed system has geospatial and mobile data sets pertaining to Bangalore city in India. It is envisaged to be helpful for touristic purposes as a route guidance and alert relaying information system to tourists for proximity to sites worth seeing in a city they have entered into. The system is modular in architecture and the novelty lies in integration of different modules carrying different technologies for a complete traffic information system. Generic information processing and delivery system has been tested to be functional and speedy under test geospatial domains. In a restricted prototype model with geo-referenced route data required information has been delivered correctly over sustained trials to designated cell numbers, with average time frame of 27.5 seconds, maximum 50 and minimum 5 seconds. Traffic geo-data set trials testing is underway. publishersversion published Document type: Article

Abstract

Traffic congestion and its associated environmental effects pose a significant problem for large cities. Consequently, promoting and investing in green travel modes such as cycling is high on the agenda for many transport authorities. In order to target investment in cycling infrastructure and improve the experience of cyclists on the road, it is important to know where they are. Unfortunately, investment in intelligent transportation systems over the years has mainly focussed on monitoring vehicular traffic, and comparatively little is known about where cyclists are on a day to day basis. In London, for example, there are a limited number of automatic cycle counters installed on the network, which provide only part of the picture. These are supplemented by surveys that are carried out infrequently. Activity tracking apps on smart phones and GPS devices such as Strava have become very popular over recent years. Their intended use is to track physical activity and monitor training. However, many people routinely use such apps to record their daily commutes by bicycle. At the aggregate level, these data provide a potentially rich source of information about the movement and behaviour of cyclists. Before such data can be relied upon, however, it is necessary to examine their representativeness and understand their potential biases. In this study, the flows obtained from Strava Metro (SM) are compared with those obtained during the 2013 London Cycle Census (LCC). A set of linear regression models are constructed to predict LCC flows using SM flows along with a number of dummy variables including road type, hour of day, day of week and presence/absence of cycle lane. Cross-validation is used to test the fitted models on unseen LCC sites. SM flows are found to be a statistically significant (p<0.0001) predictor of total flows as measured by the LCC and the models yield R squared statistics of ~0.7 before considering spatio-temporal variation. The initial results indicate that data collected using fitness tracking apps such as Strava are a promising data source for traffic managers. Future work will incorporate the spatio-temporal structure in the data to better account for the spatial and temporal variation in the ratio of SM flows to LCC flows.

Abstract

Abstract. A significant advantage of OpenStreetMap data is its up-to-dateness. However, for rural and city planning, it is also of importance to access historical data and to compare the changes between new and old versions of the same area. This paper first introduces into a differentiated classification of changes on OpenStreetMap data sets. Then a methodology for an automated database-supported analysis of changes is presented. Beyond the information already provided from the OpenStreetMap server, we present a more detailed analysis with derived information. Based on this approach it is possible to identify objects with attributive or geometric changes and to find out how they exactly differ from their previous versions. The analysis shows in which regions mappers were active during a certain time interval. Furthermore, a time based approach based on various parameters to determine the quality of the data is presented. It provides a guideline of data quality and works without any reference data. Therefore, an indication about the development of OpenStreetMap in terms of completeness and correctness of the data in different regions is given. Finally, a conclusion and an outlook on open research questions are presented.

Document type: Article

Abstract

Mishra, S., Bhattacharya, D., Gupta, A., & Singh, V. R. (2018). Adaptive traffic light cycle time controller using microcontrollers and crwodsource data of Google APIs for developing countries. In 3rd International Conference on Smart Data and Smart Cities (4/W7 ed., Vol. 4, pp. 83-90). (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences). DOI: 10.5194/isprs-annals-IV-4-W7-83-2018 Controlling of traffic signals optimally helps in avoiding traffic jams as vehicle volume density changes on temporally short and spatially small scales. Nowadays, due to embedded system development with the rising standards of computational technology, condense electronics boards as well as software packages, system can be developed for controlling cycle time in real time. At present, the traffic control systems in India lack intelligence and act as an open-loop control system, with no feedback or sensing network, due to the high costs involved. This paper aims to improve the traffic control system by integrating different technologies to provide intelligent feedback to the existing network with congestion status adapting to the changing traffic density patterns. The system presented in this paper aims to sense real-time traffic congestion around the traffic light using Google API crowdsource data and hence avoids infrastructure cost of sensors. Subsequently, it manipulates the signal timing by triggering and conveying information to the timer control system. Generic information processing and communication hardware system designed in this paper has been tested and found to be functional for a pilot run in real time. Both simulation and hardware trials show the transmission of required information with an average time delay of 1.2 seconds that is comparatively very small considering cycle time. publishersversion published Document type: Article

Abstract

YouthMappers is a global network of student chapters actively engaged in collaborative mapping efforts, such as OpenStreetMap mapathons. Many questions have been raised about the impact of mapathons on open map data and on the participating mappers. For example, how can the social gathering and event format encourage productivity and quality, while also contributing to community building? Because YouthMappers chapters regularly host mapathons, there are frequent opportunities to investigate the impact of mapathons. In this paper, three universities involved in the YouthMappers network, located in Europe, North America and Africa, describe how mapathons are conducted at their respective universities. Incorporating mapathons into the curriculum encourages students to contribute much-needed open geospatial data for humanitarian projects. At the same time, students get practical experience in data capturing with open source tools and awareness is raised of humanitarian challenges in other parts of the world, thus nurturing socially engaged citizens for the future. The experiences at the three universities are diverse and richly contextual to the specific character of the campus and its students. These differences underscore the challenge of a common means to formally assess the impact of such events in general. Based on this exploratory research, three themes for assessing the impact of mapathons are proposed: the volume and quality of open geographic data produced during mapathons; the social and personal growth of the students attending the mapathons; and the changes in university programs and curricula introduced as a result of the mapathons.

Abstract

Abstract. In developing countries, metropolitan cities, due to their economic activities, attract an increasing amount of commuters on a daily basis. This has led to major freeways and roads experiencing high levels of congestion and consequently high pollution levels. In 2020, due to a global pandemic of an outbreak of Corona Virus (COVID-19), the national government declared a national shutdown with only essential traffic being allowed to operate. Given the scenario of the national lock-down this allows for the statistical analysis of the impact of essential traffic on the overall transportation system. Consequently the aim of the paper was to assess the congestion and CO2 emission impact of essential traffic for the City of Johannesburg. Using an exploratory approach, we monitored and collected traffic congestion data from the Tomtom traffic index for the metropolitan city of Johannesburg, South Africa. We develop a relationship between congestion and pollution to visualise the daily variations in pollution and congestion levels. We demonstrate this by comparing variations in congestion levels in two epochs, viz the period without movement restrictions and the period whereby movement is restricted. The results reveal essential traffic on the congestion index to be below 22 percent for both weekends and weekdays. A scenario common only during weekends in 2019. Whilst for the emission index, CO2 levels are approximately less than 45 percent throughout the week. The paper concludes the investment into mining and analysing traffic data has a significantly role for future mobility planning in both the developed and developing world and, more generally, improving the quality of commuting trips in the city. Document type: Article