Route planning in public transport receives an increasing interest in smart cities and particularly in metropolitan cities where crowded and jammed traffic is daily recorded in the transportation network. The availability of digital footprints, such as ticketing logs, or load on board the trains, provides a relevant opportunity to develop innovative decision-making tools for urban routing of passengers in order to assist them to better plan their journeys. In this paper, we propose to enrich existing journey planners with predictive travel information to enhance the passenger travel experience during his journey. For that purpose, we augment the planned trips with predictive passenger flow indicators such as the load on board trains, and passenger attendees at the station. These indicators are forecasted along the journey with the help of the developed machine learning models. The experiments are conducted on a real historical dataset covering the Paris Region with a focus on a railway transit network that serves mainly the suburb of Paris.

Battery electric vehicles (BEVs) are playing an increasingly important role in personal mobility due to the wish to counteract climate change and political regulations concerning carbon dioxide emissions. Nevertheless, there are obstacles that need to be overcome. Especially long-distance journeys are problematic due to long charging stops and range anxiety. It is a drivers wish to fulfill a given driving task in a time-optimal way. But in the BEV case, driving faster does not necessarily lead to a decreased total travel time. The vehicle routing and charging problem is formulated as a mixed-integer nonlinear program (MINLP) and solved using mathematical optimization methods. First, time-minimizing vehicle routing with charging stations providing different powerlevels is discussed. The program returning the exact result is significantly faster than previous ones. Afterwards, the model is extended: driving speed becomes adjustable. A combined timeminimal optimization of which route to take, how fast to drive, where and how much to recharge is the result. The combination of these four parameters has never been studied before. It is shown that up to 14.48 % of driving time can be saved in our examples by incorporating the choice of a driving speed.

The number of personal cars is expected to rise at a rapid rate, particularly in developing countries, such as China and India. These increases will hamper the global effort to reach the climate target set by the Paris Agreement as most of these vehicles will be fossil-fuel powered. Urban carsharing is a Transport Demand Management measure that can reduce and delay vehicle purchasing. However, little has been reported on its utilization, particularly in the context of developing countries. This study provides empirical results on how a dominantly station-based carsharing service in Bangkok city, Thailand, is utilized. We analyzed the data generated by the service to provide descriptive information on the users' behavior. We also clustered the users into three groups; frequent users, typical car renters, and young car sharers to provide new insights into how carsharing is utilized by the Bangkokians. The outcomes provide a reference case for future studies and support policy-making to promote carsharing in a similar context.