Introduction of car sharing into existing car fleets in microscopic travel demand modelling

Microscopic travel demand models take the characteristics of every individual person of the modelled population into account for computing the travel demand for the modelled region. Car sharing is an old concept, but the combination of a car sharing fleet parked in public space with smartphone services to find available cars nearby offers a new mobility service. It enables people to use a fleet operators cars by providing individual mobility on demand. However, integrating this mobility option into microscopic travel demand models still is a difficult task due to a lack of data. This paper shows an integrated approach to model car sharing as a new mode for transport within a travel demand model using disaggregated car fleets with car specific attributes. The necessary parameters for mode choice are estimated from various surveys and integrated into an existing multi nominal logit model. The proposed work is used to simulate the travel demand of a synthetic population for the German capital of Berlin. A comparison with the survey results shows that the proposed integration of car sharing meets the real-world data. Furthermore, it is shown that the mode choice reacts well for access restrictions for specific car segments and local accessibility influencing the trip lengths

Scenarios of developing sustainable urban living environments and the role of mobility

The aim of urban development strategies to make cities safe, resilient and sustainable is directly intertwined with mobility concepts. While benefits of active mobility (walking and cycling) as well as access to public transport are generally well-recognised, in planning practise and concepts, urban development is frequently taking place without considering the association with individual mobility patterns. Current debates and plans of intra-urban development versus suburban development are rarely taking into account the effect on urban mobility and the associated challenges (e.g. traffic, noise pollution, and air quality). The aim of this poster is to present a sustainability analysis of planned urban development and an analysis of projected future mobility patterns. We focus on a local study area in the suburban district Adlershof of Berlin that has been subject to rapid development in recent years and further planned developments in the future. We use different datasets to evaluate environmental (such as NDVI to capture greenness, noise, and pollution) and social indicators (e.g. crime data) to characterize and compare different aspects of sustainability of the existing and planned urban development in a spatially explicit multi-criteria analysis. We define different scenarios, varying the weights for more social or ecological preferences and evaluate the areas according to these scenarios. Moreover, we use data from a mobility survey we conducted in Adlershof with detailed information on mobility patterns. Finally, we apply an ABM (TAPAS) and microsimulation (SUMO) to model future transport demand for different modes of transport (cycling, PT, car) and the expected traffic load in the network. Our findings identify maps and distinct plots according to the social and ecological preferences. Most sustainable areas for housing development are identified. The impact of the expected growth in the area on the transport system is estimated and related concerns are discussed. Finally, we identify future key research challenges: How can we combine spatially explicit urban land use development models and scenarios with mobility models? Which role can scenario-analysis play for discussing current urban development strategies. With our analysis we show how important the intertwined perspective from urban development and mobility concepts is and that indicator- and model-based approaches can provide important insights for different urban scenarios

Effects of different mobility concepts in new residential areas

Growing cities need new residential areas, which are often either not connected to the existing transport infrastructure or are poorly connected to it. A fast way to connect these areas is the construction of roads. However, this generates a car- depending mobility among the inhabitants, which is in conflict with several sustainability goals. Moreover, the impact of the implementation of new public transport options is only partly known and this fact reduces the willingness to invest in expensive public transport measures. In this work we examine different mobility concepts, including shared mobility, bicycle highways, a high-frequency bus service, suburban trains and car limitations in a new residential area of 2000 households in Berlin, Germany, which is currently under construction. The households and inhabitants are created synthetically using statistical data derived from a survey among the first people moved in. The age and size structure of these households turn out to be different from the neighbouring households. Then, we implement all measures in a microscopic travel demand simulation and quantify the potential modal shifts for four different mobility concepts. The results show that weak and short-term mobility concepts show no significant change in mobility behaviour. Only highly integrated projects like bicycle highways into the inner city combined with suburban trains can reduce the need for car-dependent mobility. Shared mobility only fills in the gaps for special occasions but not for daily mobility due to the high costs. In a final step we examine the usage of the introduced public transport services and compare the change in the occupation of the buses and trains. Here our work shows that interchanging from bus to subways and suburban trains drastically reduces the attractiveness of public transport. Introducing a new suburban train changes this situation and the whole region shows a drop of 40% of car trips

Introduction of car sharing into existing car fleets in microscopic travel demand modelling

Microscopic travel demand models take the characteristics of every individual person of the modelled population into account for computing the travel demand for the modelled region. Car sharing is an old concept, but the combination of a car sharing fleet parked in public space with smartphone services to find available cars nearby offers a new mobility service. It enables people to use a fleet operators cars by providing individual mobility on demand. However, integrating this mobility option into microscopic travel demand models still is a difficult task due to a lack of data. This paper shows an integrated approach to model car sharing as a new mode for transport within a travel demand model using disaggregated car fleets with car specific attributes. The necessary parameters for mode choice are estimated from various surveys and integrated into an existing multi nominal logit model. The proposed work is used to simulate the travel demand of a synthetic population for the German capital of Berlin. A comparison with the survey results shows that the proposed integration of car sharing meets the real-world data. Furthermore, it is shown that the mode choice reacts well for access restrictions for specific car segments and local accessibility influencing the trip lengths

Usage Trend Analysis and Forecasting for Ride Sharing: A case of Bildeleringen : An empirical approach using the car-specific data

Car-sharing is gaining a lot of popularity amongst users, as more people are finding various instances and benefits to use this service. With this development, there is increasing number of companies setting up car-sharing platforms to satisfy this growing demand. As is characteristic of highly competitive industries, the players win market share by effective planning and efficient operations. One aspect of effective planning is ensuring that the carsharing fleet of cars is suitable to the needs of the target customers. The goal of this paper is to use past data to analyse the car features that are affecting the demand of cars and propose a model to predict the future demand of cars using these features. To achieve this, we obtained the ride data from Bildeleringen, the leading car sharing operator in Bergen (Norway). We analysed all of the data tables and picked the variables that were essential to our study. After cleaning up the data, we created a new dataset that gave car level information on the car type, car features, the availability period, and the usage variable. We obtained two measures of usage from the data – time driven and kilometres driven. Based on the business model of Bildeleringen where more of the cost of usage is attributed to the driven time, we chose the time driven as the more appropriate usage measure. Also, we noticed that some cars were available on the platform for way longer than others, hence we went a step further to define the measure of usage as the kilometre driven as a ratio of the time available on the platform. Using charts, histograms, and box plots, we investigated the possible relation in the car features and the usage of these cars on first glance. We then proceeded to run a multiple linear regression on our data set. We then used 10 data prediction methods to model the car usage and tested the predictive performance of the models using cross validation. The models used belonged to the Linear regression, Ensembles, Decision tree, Bagging and Boosting. The results of the show that are the car level features that affect the demand are transmission type, wheel drive system, baby pillow availability, child seat installed, and roof box installed. Based on the Mean Squared Error comparison, we also found that the Decision tree is the best model to use for the prediction.nhhma

Multi-agent Spatiotemporal Simulation of Autonomous Vehicle Fleet Operation

Autonomous vehicle fleets, consisting of self-driving vehicles, are at the forefront of transportation innovation. The appearance of autonomous vehicles (AVs) provides a new solution for traffic problems and a new market for transportation network companies such as DiDi and Uber. Conducting simulations in the present is indeed crucial to prepare for the eventual operation of autonomous vehicles, as their widespread adoption is expected to occur in the near future. This research adopts an Agent-Based Modelling (ABM) approach to understand and optimize the performance of autonomous vehicle systems. Moreover, Geographic Information System (GIS) technology also plays a crucial role in enhancing the effectiveness and accuracy of the simulation process. GIS enables the representation and manipulation of geospatial data, such as road networks, land-use patterns, and population distribution. The combination of ABM and GIS allows for the incorporation of real-world geographic data, providing a realistic and geographically accurate environment for the agents in the virtual environment. In this thesis, the multi-agent spatiotemporal simulation is conducted by the GAMA platform. The model simulates the behaviour and interactions of individual agents, which are fleet agents and commuters, to observe the emergent behaviour of the entire system. Within the experiment, different scenarios are considered for both people and fleets to explore a range of approaches and strategies. These scenarios aim to evaluate the effectiveness of various approaches in meeting dynamic commute needs and optimizing fleet operations. By simulating these different scenarios and analyzing their outcomes, the study aims to provide insights into the improvement of fleet size and deployment in autonomous vehicle systems. The ultimate goal is to identify effective strategies that lead to optimized fleet size in different scenarios, reduced idling time and emission, improved traffic management, and overall more efficient and sustainable autonomous vehicle systems