Forecasting the state of health of electric vehicle batteries to evaluate the viability of car sharing practices

Car sharing practices are introducing electric vehicles into their fleet. However, literature suggests that at this point shared electric vehicle systems are failing to reach satisfactory commercial viability. Potential reason for this is the effect of higher vehicle usage which is characteristic for car sharing, and the implication on the battery state of health. In this paper, we forecast state of health for two identical electric vehicles shared by two different car sharing practices. For this purpose, we use real life transaction data from charging stations and different electric vehicles’ sensors. The results indicate that insight into users’ driving and charging behaviour can provide valuable point of reference for car sharing system designers. In particular, the forecasting results show that the moment when electric vehicle battery reaches its theoretical end of life can differ in as much as ¼ of time when vehicles are shared under different conditions

Forecasting transport mode use with support vector machines based approach

The paper explores potential to forecast what transport mode one will use for his/her next trip. The support vector machines based approach learns from individual's behavior (validated GPS tracks) to support smart city transport planning services. The overall success rate, in forecasting the transport mode, is 82 %, with lower confusion for private car, bike and walking

Sensing Human Activity for Smart Cities’ Mobility Management

Knowledge about human mobility patterns is the key element towards efficient mobility management. Traditionally, these data are collected by paper/phone household surveys or travel diaries and serve as input for transportation planning models. In this chapter, we report on current state-of-the-art techniques for sensing human activity and report on their applicability for smart city mobility management purposes. We particularly focus on the use of location-enabled devices and their potential towards replacing traditional data collection approaches. Furthermore, to illustrate applicability of smartphones as ubiquitous sensing devices we report on the use of Routecoach application that was used for mobility data collection in the city of Leuven, Belgium. We provide insights into lessons learned, ways in which collected data were used by different stakeholders, and identify existing gaps and future research needs in this field

A collaborative stakeholder decision-making approach for sustainable urban logistics

Cities strongly rely on efficient urban logistics to ensure their attractiveness, quality of life, and economic development. In the same time, they strive to ensure livable and safe environments around its road network, where the increased presence of light and heavy goods vehicles raises questions of regarding safety and environmental impacts. Recent literature has well-recognized the need to consider different stakeholders’ perspectives on these issues, in order to achieve desired outcomes. In this paper, we introduce a collaborative stakeholders’ decision-making approach for sustainable urban logistics, and demonstrate its applicability on a real-life example. The suggested approach extends existing route planning approaches by considering route sustainability as a part of an arc’s traversal cost. The integration of route sustainability is based on the adoption of a multi-criterial decision-making approach, with the possibility of including different stakeholders’ points of view, and evaluating the sustainability cost concerning the route’s spatial context. To demonstrate the applicability of the suggested approach, we extract the route sustainability cost from the traffic sign database, and implement the findings on a real-life example. Furthermore, the suggested approach exhibits a high level of transferability to various local contexts, where local stakeholders might have a different view on the route sustainability than is the case in our exampl

The MOVE platform: monitoring urban travel

Status and applications of the MOVE platform for monitoring traval behavior by means of a smartphone ap

The moving crowd: collecting and processing of crowd behaviour data

The MOVE project focuses on the collection and analyses of crowd behavior data. The two main goals of the project are first, the collection of data through mobile phones. The second goal is to develop new technologies to process and mine the collected data for crowd behaviour analysis. The technology will allow to make advanced interpretations of historic and dynamic mobile crowd data coming from GSM/GPS and from different classes of users (vehicle, pedestrian, indoor/outdoor). Fusion will be made between data coming from different sources (smartphone, navigation device) and external map data. The interpretation will allow the mining of advanced features/geometry from the crowd data as well as interprete the dynamic behaviour of the population