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Smart cities and communities rely on efficient, reliable and robust transport systems. Managing urban public transport systems is becoming increasingly challenging with a pronounced shift towards multiple actors operating in a multi-modal multi-level networks. This calls for the development of an integrated passenger-focused management approach which takes advantage of multiple data sources and state-of-the-art scheduling support. The TRANS-FORM project is developing, implementing and testing a data driven decision making tool that will support smart planning and proactive and adaptive operations. The tool will integrate new concepts and methods of behavioral modelling, passenger flow forecasting and network state predictions into real-time operations. In this study we present the first step in this direction which consists of an empirical analysis of passenger flows to infer travel patterns and service reliability properties. Data mining and transport flow analysis are used to investigate network dynamics at different scales.

Greenhouse gas emissions related to energy production is the main cause of climate change. Transportation accounts for 30% of the total energy consumption, and a reduction in the energy used for mobility is necessary. The 2000-watt society is an environmental concept that fixes a sustainable limit to the energy consumption in different sectors, including mobility. This paper evaluates the energy consumption in several mobility scenarios, and it assesses whether the goal suggested by the 2000-watt society is achievable. We investigate the social characteristics and mobility habits of the population living in a case study area. Then, we calculate the modal shift induced by transportation policies such as car-sharing, car-pooling and car-free district. We evaluate the resulting energy consumption, and we compare it with the 2000-watt society limit. We conclude that only a set of measures combining car usage reduction, increase in walking and cycling, and reduction in the total travel distance can achieve the needed energy reduction.

In collaboration with the Swiss Federal Railways (SBB-CFF-FFS), various challenges associated with pedestrian flows in train stations are discussed at the example of Lausanne railway station. For this site, a rich set of data sources including travel surveys, pedestrian counts and trajectories has been collected. The report is organized in three parts. First, an empirical analysis of the aforementioned data sources is provided. The main focus thereby lies on the identification of periodical movement patterns both in time and in space. Second, a methodology for estimating pedestrian origin- destination (OD) demand using various information sources including the train timetable is discussed. This methodology is applied to the case of Lausanne railway station, and results are provided for the morning peak period. Third, a pedestrian flow model is presented which, for a given OD demand, allows to estimate pedestrian travel times and density levels based on an empirical pedestrian fundamental diagram. This model is applied to study pedestrian movements in an underpass of Lausanne railway station, including an assessment of pedestrian level of service. Instead of focusing on mathematical details, the present document provides a general overview of the problem of modeling pedestrian flows in railway stations that is accessible to practitioners. Suggestions for further literature are provided throughout the document.

Forecasts predict a strong increase of the demand of public transport facilities in the next decades, close to doubling the number of users in the "Arc lémanique" area. This increase in demand can lead to congestion and poor level-of-service inside transportation hubs. The aim of this paper is to develop control strategies which aim at guiding pedestrians in hubs and improving the flow dynamics. The objective is to guarantee adequate level-of-service throughout the infrastructure. This is achieved by influencing the locations pedestrians dwell while waiting or the activities they perform before or after using a public transport mode. Not only are "hard" strategies developed (barriers for example) but "soft" strategies like information or advertisement are also used. These measures are coupled with a dynamic assignment and demand model to simulate the effect on pedestrian flows. To quantify the impact of such management strategies, a simulation framework is developed and the results are compared to real-life data collected in the train station of Lausanne. Through this framework, pedestrian control measures can be tested and validated before being implemented in practice, and the impact of such decisions can be maximized, furthermore the performance of the facility is improved.

Pedestrian mobility is at the center of urban transportation systems. Therefore, understanding it is a necessary task to improve the mobility in cities. However, pedestrian mobility is extremely complex, and advance methods are needed to capture its dynamics. This chapter presents the main methods used to describe, model and control pedestrian behavior. The chapter introduces the fundamental relationships describing pedestrian dynamics, models representing the demand and assignment of pedestrians and, to conclude, innovative management strategies.

Exploiting the full potential of pedestrian infrastructures in order to satisfy the demand induced by public transport modes is key to achieving good level-of-service for passengers during transfers. High temporal variability in demand can lead to high congestion and possibly dangerous situations while the infrastructure is underused moments after. In order to improve the level-of-service experienced by pedestrians, two management strategies are investigated. Firstly, the utilization of gates to control the flows of passengers moving around the station is explored thanks to a PI control structure. Secondly, counter flow is minimized by dynamically adapting the space to the flows moving in opposing directions. This can be achieved by separating the corridor into two parts, one dedicated to each flow direction. The station of Lausanne, Switzerland is used as a case study for these strategies. Gating proved e ffi cient to limit congestion without significantly increasings the travel time of passengers. The expected results when separating counter flow are similar: an increase in comfort without negatively impacting travel time. Finally, the implementation of e ffi cient management strategies for pedestrians can significantly improve the transfer experience in transportation hubs by controlling the walking times between services.

We introduce a modeling approach for pedestrian speed-density relationship. It is motivated by a high scatter in real data that precludes the use of traditional equilibrium relationships. To characterize the observed pattern we relax the homogeneity assumption of equilibrium relations and propose a multi-class model. In addition to the general modeling framework, we also present some concrete model specifications. Real data is utilized to test the performance of the approach. The approach is able to reveal fundamental properties causing the heterogeneity in population and describe their impact on pedestrian movement. We also show the advantages of the proposed approach compared to approaches from the literature. The proposed model is flexible, and it provides better fit and richer information than traditional models.

Starting from the assumption of a future where the use of cars will be limited in dense urban areas, and thus streets could be used for innovative transport modes, this project investigates a system based on accelerated moving walkways (AMW). Differently from constant moving walkways, AMW can reach speeds up to 15km/h thanks to an acceleration section. This paper, after a review of existing AMW implementations, presents a description of this system and formulates an optimization problem to identify the optimal design of a network of AMW. Decision variables and parameters are defined for a general scenario, and five criteria are considered for the formulation of the objective function: travel time, discomfort, energy consumption, construction cost and operational cost. The resulting multi-objective mixed integer nonlinear optimization problem is evaluated for the simple case of a single link, and results of optimal speed profiles for different parameter values are presented. A network of accelerated moving walkway in a car-free urban environment may present an innovative solution, and further research at a network level could delineate the system feasibility.

Transportation hubs, such as airports and train stations, tend to experience congestion as their recent diversication of services attracts more people and the demand for mobility keeps increasing, while the expansion possibilities of the infrastructure are limited. Moving walkways, whose use is widespread to deal with long walking distances, can be a key infrastructure to reduce conges- tion and travel time. We develop an optimization framework which explores the disposition of moving walkways in transportation hubs with respect to two contrasting objectives: minimizing both the total travel time and the total in- stallation (capital) cost. We dene ow-dependent walking speed functions to model congestion. Its in uence on the route choice made by the pedestrians is then included thanks to a user-equilibrium formulation. This methodology is applied to the future train station of Lausanne, Switzerland. Simulations yield positive results with respect to travel time savings and also reveal chal- lenging aspects linked to the intersection between pedestrian ows and moving walkways.