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    Posted in July 2018

    Improving railway passengers experience: two perspectives

    This paper describes two perspectives to improve the passenger experience. The passenger satisfaction pyramid is introduced, consisting of the base of the pyramid (dissatisfiers) focusing on time well saved and the top of the pyramid (satisfiers) aiming at time well spent. The challenge in planning and design of public transport services is to find the most efficient (set of) design choices. Depending on the context this might either mean focusing on the top or on the bottom of the pyramid. We found that influencing and enhancing the qualities of the satisfiers is far more important than traditional studies showed us. For stations, regression analyses show that dissatisfiers are responsible for explaining almost half of the total score of the station and satisfiers are responsible for the other half of the scores passengers give for the station. We still have to put a lot of energy in getting the basics right, starting in the planning phase, but then we are not allowed to lean back. We have to keep investing in qualities like ambience, comfort and experience which makes the customers truly happy at the end of the day.

    Read our paper HERE and find the presentation HERE

    Supervised learning: Predicting passenger load in public transport

    For many Public Transport (PT) users, overcrowding in PT vehicles has a major decreasing effect on the comfort experience. However, most online routing applications still not take comfort regarding to crowdedness into account, but provide recommendations based on shortest distance, shortest travel-time, or number of interchanges.
    Being able to include certain information on crowdedness, requires knowledge about the current and future level of passenger load. Increasing amount and complexity of data describing public transport services allows us to better explore the detection methods and analysis of different phenomena of PT operations. Some countries or operators provide the possibility to use Smart Card (SC) data for occupancy prediction. However, SC data is not available in real time, which makes it hard to incorporate it into real time recommendation models. In this work, we show that it is possible to predict the passenger load via supervised learning, eliminating the need for fare collection data beyond the set needed for training.

    Find the CASPT presentation by Léonie Heydenrijk-Ottens HERE

    Driver schedule efficiency vs. public transport robustness: A framework to quantify this trade-off based on passive data

    More complex, efficient driver schedules reduce operator costs during undisrupted operations, but increase the disruption impact for passengers and operator once a disruption occurs. We develop an integrated framework to quantify the passenger and operator costs of disruptions explicitly as function of different driver schedule schemes. Since the trade-off between driver schedule efficiency and robustness can be quantified, this supports operators in their decision-making.

    Read the CASPT paper by Menno Yap HERE and find the presentation HERE

    Assessing disruption management strategies in rail-bound urban public transport from a passenger perspective

    This paper provides a framework for generating and assessing alternatives
    in case of disruptions in rail-bound urban public transport systems,. The proposed
    framework considers the passenger perspective as well as the operator perspective,
    for the often-used measures of detouring and short-turning. An application of the
    framework demonstrates that currently used disruption management protocols often
    do not lead to the optimal solution from the passenger perspective. Furthermore, the
    optimal choice between alternatives from passenger perspective shows to be
    dependent on the passenger flows.

    Read the CASPT paper HERE and find the presentation HERE

    Passenger Route Choice and Assignment Model for Combined Fixed and Flexible Public Transport Systems

    The recent technological innovations have given rise to innovative mobility solutions. Public transport systems combining such services need novel models for the design of services. We develop a multimodal route choice and assignment model for combined use of line/schedule based public transport systems (fixed public transport) and demand responsive services (flexible public transport). The model takes into account the dynamic demand-supply interaction using an iterative learning model framework. Flexible public transport can be used to perform any part of the trip, ranging from a first/last mile service to an exclusive direct door-to-door connection. The developed model is implemented in an agent based simulation framework. The model is applied to the test network of Sioux Falls. Results, in terms of modal split, fleet utilization, and passenger waiting times are analysed for scenarios in which fixed and flexible public transport are offered as competing modes as well as potential complementing modes.

    Find the CASPT presentation HERE

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