Public transport disruptions can result in major impacts for passengers and operator. Our study objective is to predict disruption exposure at different stations, incorporating their location-specific characteristics. Based on a 13-month incident database for the Washington metro network, we successfully develop a supervised learning model to predict the expected number of disruptions, per type, station and time of day. This supports public transport authorities and operators to prioritize what type of disruptions at what location to focus on, to potentially achieve the largest reduction in disruption exposure. Our clustering results show that start/terminal and transfer stations are most susceptible to disruptions, mainly due to operations-and vehicle-related disruptions.

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Artificial intelligence heeft duidelijk z’n intrede gedaan in het ov. Dat bleek tijdens het internationale Conference on Advanced Systems of Public Transport (CASPT) deze zomer down under in Brisbane. Tien onderzoekers van het nieuwe Delftse Smart Public Transport Lab waren present. Een verslag.@en

Robust public transport networks are important, since disruptions decrease the public transport accessibility of areas. Despite this importance, the full passenger impacts of public transport network vulnerability have not yet been considered in science and practice. We have developed a methodology to identify the most vulnerable links in the total, multi-level public transport network and to quantify the societal costs of link vulnerability for these identified links. Contrary to traditional single-level network approaches, we consider the integrated, total multi-level PT network in the identification and quantification of link vulnerability, including PT services on other network levels which remain available once a disturbance occurs. We also incorporate both exposure to large, non-recurrent disturbances and the impacts of these disturbances explicitly when identifying and quantifying link vulnerability. This results in complete and realistic insights into the negative accessibility impacts of disturbances. Our methodology is applied to a case study in the Netherlands, using a dataset containing 2.5 years of disturbance information. Our results show that especially crowded links of the light rail/metro network are vulnerable, due to the combination of relatively high disruption exposure and relatively high passenger flows. The proposed methodology allows quantification of robustness benefits of measures, in addition to the costs of these measures. Showing the value of robustness, our work can support and rationalize the decision-making process of public transport operators and authorities regarding the implementation of robustness measures.

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The passenger impact of a disruption on the train network can propagate over the multi-level public transport (PT) network, via the transfer hub to the urban PT network. Hence, an optimal holding control decision for urban services at the transfer location should account for the impact of a disruption on another PT network level. Modelling framework We first quantify the passenger impacts of disruption propagation resulting from an exogenous train network disruption to the urban PT network level. Thereafter, we develop a rule-based controller for holding urban PT services while taking into account predicted passenger delays and rerouting from the train network level caused by the train network disruption. This means that in this study a control decision is triggered by services which are not subject to this same control decision. Scenario design We quantify the total passenger welfare for three different scenarios, expressed as the generalized travel time over all passengers: -Scenario 1: undisrupted train network; no urban control intervention; -Scenario 2: train network disruption; no urban control intervention; -Scenario 3: train network disruption; urban control intervention. Control problem description The applied control strategy entails the decision whether to hold urban PT runs at multi-level transfer stops for a certain holding time in case a disruption occurs on the train network. The predicted welfare impacts on four different passenger segments are incorporated in this holding decision: (i) Upstream boarding and downstream alighting (through) passengers; (ii) Downstream boarding passengers; (iii) Reverse downstream boarding passengers; (iv) Transferring passengers at holding location. A passenger-oriented decision rule is applied for the controller, where predicted costs of the control decision are deducted from the predicted control benefits for all passenger segments, aimed at minimizing passenger travel costs on the urban network. Holding results in a direct extension of in-vehicle time at the holding stop of passengers who board upstream the holding location and alight downstream the holding location, and a waiting time extension for downstream boarding passengers, corrected for turnaround buffer time for reverse downstream boarding passengers. Besides, holding reduces waiting time for passengers transferring at the holding location, compared to having to wait for the next service. The holding strategy also affects the different passenger segments in terms of perceived in-vehicle time due to changed crowding levels. Due to the non-linear nature of perceived in-vehicle time as function of crowding, we quantify crowding effects over all passenger segments simultaneously. Application We apply our methodology to the multi-level PT network of The Hague, the Netherlands. BusMezzo, an agent-based dynamic simulation model for PT operations and passenger assignment, is used as evaluation tool. @en

In daily practice, public transport authorities and operators are constantly searching for improvements in public transport operations. To this end, it is necessary to identify inefficiencies and bottlenecks in the current public transport services. In this paper, we propose a method to automatically detect bottlenecks in the public transport network, using Automatic Vehicle Location data. A tool is developed to automatically process AVL data to identify bottlenecks for the current situation. This tool is applied to Amsterdam, capital of the Netherlands, where a new metro line will come into operation in the summer of 2018. The results show that bottlenecks are mainly found on radial lines and in the inner city. Therefore we expect that the operations of the tram network will improve in terms of operating speed and reliability due to the opening of the metro line, since the tram lines are expected to become less crowded and fewer lines will traverse the inner city.@en

For large urban networks and hubs, optimizing transfer synchronization becomes computationally challenging. The objective of this paper is therefore to develop a generic, data-driven methodology to determine the key line/direction-combinations to synchronize based on passenger flows. We developed an approach to detect communities of directional lines based on passenger transfer flows, by calculating modularity using a C-space inspired network representation. Our results show intuitive clusters to prioritize for synchronization on a network level for tactical planning, and on the hub level for real-time coordination.@en

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.@en

This paper synthesizes evidence from Workshop 8 ‘Big data in the digital age and how it can benefit public transport users’ of the 15th International Conference on Competition and Ownership in Land Passenger Transport. Big data in public transportation has increasingly attracted the attention from both scientists and practitioners, resulting in an increasing number of scientific studies and practical applications in this field. However, compared to the scientific developments, we see that practical big data applications are relatively limited, and that these are applied with a relatively low pace. This indicates that big data has not been used to its full potential in practice yet, meaning that public transport passengers currently do not fully benefit from the opportunities big data offers in terms of public transport quality and attractiveness. Based on literature study and input gained from a qualitative expert session with scientists, public transport authorities, public transport operators and transport consultants together during the conference workshop, we come to the conclusion that the challenges to stimulate further and faster use of big data in practice are institutional rather than technical. This complexity results from required coordination and cooperation among public and private entities that are not always aligned. A framework has been proposed with four components to stimulate a further and faster adoption of big data in practice, directing to different stakeholders or relations between stakeholders: align technical ambitions of big data applications with their institutional environment; enable/ease the use of big data by PT authorities by developing common definitions, data standards and consolidation; incorporate the use of big data by PT operators in the contract between authority and operator; quantify and visualize the business value of big data for PT operators. We illustrate our framework by successful case studies in Chile, the Netherlands and Sweden.

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Crowding in public transport can be of major influence on passengers’ travel experience and therefore affect route and mode choice. In this study, crowding valuation for urban tram and bus travelling is determined fully based on revealed preference data. Urban tram and bus crowding valuation is estimated in a European context based on a Dutch case study network. Based on the estimated discrete choice model, we conclude that crowding plays a significant role in passengers’ route choice in public transport. The average crowding multiplier of in-vehicle time equals 1.16 when all seats are occupied. For frequent travellers, this value is equal to 1.31. Our study results suggest that infrequent travellers do not incorporate expected crowding in their route choice. The insights gained from our study can support the decision-making process of policy-makers, by quantifying the benefits of measures aiming to reduce crowding levels for example in a cost–benefit analysis framework.

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The availability of smart card data from public transport travelling the last decades allows analyzing current and predicting future public transport usage. Public transport models are commonly applied to predict ridership due to structural network changes, using a calibrated parameter set. Predicting the impact of planned disturbances, like temporary track closures, on public transport ridership is however an unexplored area. In the Netherlands, this area becomes increasingly important, given the many track closures operators are confronted with the last and upcoming years. We investigated the passenger impact of four planned disturbances on the public transport network of The Hague, the Netherlands, by comparing predicted and realized public transport ridership using smart card data. A three-step search procedure is applied to find a parameter set resulting in higher prediction accuracy. We found that in-vehicle time in rail-replacing bus services is perceived ≈1.1 times more negatively compared to in-vehicle time perception in the initial tram line. Waiting time for temporary rail-replacement bus services is found to be perceived ≈1.3 times higher, compared to waiting time perception for regular tram and bus services. Besides, passengers do not seem to perceive the theoretical benefit of the usually higher frequency of rail-replacement bus services compared to the frequency of the replaced tram line. For the different case studies, the new parameter set results in 3% up to 13% higher prediction accuracy compared to the default parameter set. It supports public transport operators to better predict the required supply of rail-replacement services and to predict the impact on their revenues.

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Vervoer op afroep, al dan niet automatisch en elektrisch, heeft zijn intrede gedaan en is niet meer te stoppen. Dat was de boodschap tijdens het TBR-congres in Washington, het jaarlijkse walhalla voor elke ov-onderzoeker.@en

Disruptions in public transport have major impact on passengers and disproportional effects on passenger satisfaction. The availability of smart card data gives opportunities to better quantify disruption impacts on passengers’ experienced journey travel time and comfort. For this, accurate journey inference from raw transaction data is required. Several rule-based algorithms exist to infer whether a passenger alighting and subsequent boarding is categorized as transfer or final destination where an activity is performed. Although this logic can infer transfers during undisrupted public transport operations, these algorithms have limitations during disruptions: disruptions and subsequent operational rescheduling measures can force passengers to travel via routes which would be non-optimal or illogical during undisrupted operations. Therefore, applying existing algorithms can lead to biased journey inference and biased disruption impact quantification. We develop and apply a new transfer inference algorithm which infers journeys from raw smart card transactions in an accurate way during both disrupted and undisrupted operations. In this algorithm we incorporate the effects of denied boarding, transferring to a vehicle of the same line (due to operator rescheduling measures as short-turning), and the use of public transport services of another operator on another network level as intermediate journey stage during disruptions. This results in an algorithm with an improved transfer inference performance compared to existing algorithms.@en

Overstappen heeft een grote invloed op de (on)betrouwbaarheid van het OV, een belangrijk kwaliteitsaspect voor veel reizigers. In eerdere studies wordt de impact van onbetrouwbaarheid van overstappen op de totale reistijd voor meerdere overstaprichtingen niet expliciet meegenomen. In deze studie presenteren we onderzoek naar synchronisatie van overstappen en de impact hiervan op betrouwbaarheid vanuit een reizigersperspectief. Reizigersbetrouwbaarheid is geanalyseerd voor een overstaplocatie tussen trein en tram, geëxploiteerd door twee verschillende vervoerders. We hebben een methode ontwikkeld om twee op de reiziger gerichte betrouwbaarheidsindicatoren te kwantificeren: extra reistijd ten opzichte van de dienstregeling, en benodigde buffertijd als maat voor spreiding. Door de beschikbaarheid van voertuiglocatie (NDOV) data en anonieme OV Chipkaartdata is het mogelijk om de betrouwbaarheid van een overstaplocatie, of van een OV netwerk als geheel, op netwerkniveau te kwantificeren. Vijf belangrijke variabelen zijn geïdentificeerd die betrouwbaarheid van een overstap kunnen beïnvloeden: de geplande overstaptijd, de verdeling van de werkelijke aankomst- en vertrektijden van de lijnen bij de overstaplocatie, frequentie van OV lijnen, overstap-looptijd, en het reizigersvolume. Voor zowel een hypothetisch OV netwerk, als voor een werkelijk OV netwerk, is aangetoond dat het verschuiven van de overstaptijd om één specifieke overstap te optimaliseren, effect heeft op de overstaptijd en betrouwbaarheid van andere overstappen. Deze methodiek geeft kwantitatief inzicht in deze trade-offs om uiteindelijk een optimum te vinden voor alle overstappen en reizigers samen. Dit kan worden meegenomen in maatschappelijke kosten-baten analyses. Met deze methodiek kunnen zowel de totale betrouwbaarheidsbaten, als de baten voor een specifieke groep reizigers, geïdentificeerd worden als gevolg van een bepaalde betrouwbaarheidsmaatregel.@en

Crowding in public transport can be of major influence on passengers’ travel experience and therefore affect route and mode choice. Therefore, it is important to understand how crowding in urban public transport is perceived by passengers. The availability of individual smart card transactions allows us to gain insights in revealed trade-offs between travel time, transfers, waiting time and crowding in public transport route choice, instead of using stated preference experiments. Our study is the first in which crowding valuation for urban tram and bus travelling is determined fully based on revealed preference data. Based on the estimated discrete choice model, we conclude that crowding plays a significant role in passengers’ route choice in public transport. The crowding multiplier of in-vehicle time equals 1.16 when all seats are occupied. The crowding multiplier increases further from 1.16 to 1.40, in case of an average density of 4 standing passengers per 푚푚2. Besides, our estimation results confirm the existence of a tram bonus, in which in-vehicle time in a tram is perceived 0.6 times the in-vehicle time perceived in a bus. The insights gained from our study can support the decision-making process, by quantifying benefits of measures aiming to reduce crowding levels.@en

This chapter deals with the Dutch smart card system, the so-called OV-Chipkaart and illustrates a potential application of its data. This chapter explores options for using this anonymous smart card data for evaluation, analysis and performing simple what-if analyses by using transport planning software. The objective is to process the data in such a way that it enhances evaluation and prediction of ridership (patterns). This helps to improve network and time-table design. The main contribution of this research is to introduce smart card as a data source into existing methods to come to a new ridership prediction approach. Our approach takes comfort into account, since it is a relevant quality indicator, which is often neglected. We show that the effect of a frequency increase in a congested public transport line in terms of additional passengers becomes significantly larger when comfort effects are included. Our approach was applied as a case study to the tram network in The Hague. The approach proved very valuable to gain insights on the effect of changes in the public transport supply. @en

Vervoer op afroep, al dan niet automatisch en elektrisch, heeft zijn intrede gedaan en is niet meer te stoppen. Dat was de boodschap tijdens het TBR-congres in Washington, het jaarlijkse walhalla voor elke ov-onderzoeker.@en

De last-mile in een openbaar vervoer (OV)reis is een van de meest hinderlijke gedeelten van een reis perOV,hierdoor is het OV veelal niet in staat om te kunnen concurreren met de auto. De oorzaak kan deels worden gevonden in het gebrek aan flexibiliteit en de lage snelheden die de veelal conventionele vervoersmiddelen op de last-mile kenmerken.Recente technologische ontwikkelingen makeninnovatieve vraaggestuurde vervoersconceptenmet zelfrijdende voertuigen mogelijk. Deze zijn onafhankelijk van vaste infrastructuur en zouden ideaalop de last-mile ingezetkunnen worden, om zo het diffusepatroon en lage vervoersvolume efficiënt tebedienen. Om de vervoerwaarde en de prestatie van een vraaggestuurd vervoersysteem met zelfrijdende voertuigen op de last-mile te bepalen is alscase:Station Delft-Zuid –TU Delft gekozen.Bij een vervoerwaarde studie metdergelijke innovatieve techniek wordt verwachtdat psychologische factoren, naast depuur instrumentele aspecten, expliciet een rol spelen in de vervoerwijzekeuzevan reizigers. Het meenemen van deze preferenties is daarom van belang, teneindede vervoerwaarde niet substantieel te over-of onderschatten. In dit paper zijnde uitkomsten van twee onderzoeken gecombineerd, een instrumenteel onderzoek (simulatiemodel + enquête) naar de vervoerwaarde van een systeem van zelfrijdende voertuigen en een stated preference (SP)-experiment om de invloed van diversepsychologische factoren op de vervoerwaarde te bepalen.De resultaten laten zien dat er een aanzienlijke vervoerwaarde bestaat voor zelfrijdende voertuigen op de last-mile, te weten57% van de steekproefpopulatie.Methetsimulatiemodel zijn diverse ITS maatregelengesimuleerd, deze maatregelen omvatten wijzigingen in de netwerkstructuur, voertuiggedragen het reizigersgedrag.De prestatie van het last-mile systeem, bleekveelalpositiefbeïnvloedt te worden door bovenstaande maatregelen.Effecten werden gemeten in een vergroting van de systeemcapaciteit, een reductie van de gemiddelde wachttijd ofwel van de gemiddelde reistijd. Het parallel toepassen van deze maatregelen realiseerteen aantrekkelijkere concurrentiepositie ten opzichte van de conventionele vervoerswijzen. De meest kenmerkende maatregel,het strategisch plaatsen van voertuigen,op locaties voorafgaand aan het ontstaan van vervoersvraag laat een reductie in de gemiddelde wachttijd van 40% zien.Uit het SP-experimentblijkt dat de attitudes ‘duurzaamheid’ en ‘vertrouwen’ de twee belangrijkste aspectenzijn in de vervoerwijzekeuze, terwijl de in-voertuigtijd niet minder negatief wordt ervaren dan in een door de reiziger gereden carsharing systeem. Dit suggereert dat de gebruikelijk genoemde voordelen van zelfrijdende voertuigenwellicht nog niet worden ervaren door de reiziger vanvandaag, en illustreert het belang om hier aandacht aan te schenken voor een succesvolle implementatievan zelfrijdende voertuigen.@en

In the recent years many developments took place regarding automated vehicles (AVs) technology. It is however unknown to which extent the share of the existing transport modes will change as result of AVs introduction as another public transport option. This study is the first where detailed traveller preferences for AVs are explored and compared to existing modes. Its main objective is to position AVs in the transportation market and understand the sensitivity of travellers towards some of their attributes, focusing particularly on the use of these vehicles as egress mode of train trips. Because fully-automated vehicles are not yet a reality and they entail a potentially high disruptive way on how we use automobiles today, we apply a stated preference experiment where the role of attitudes in perceiving the utility of AVs is particularly explored in addition to the classical instrumental variables and several socio-economic variables. The estimated discrete choice model shows that first class train travellers on average prefer the use of AVs as egress mode, compared to the use of bicycle or bus/tram/metro as egress. We therefore conclude that AVs as last mile transport between the train station and the final destination have most potential for first class train travellers. Results show that in-vehicle time in AVs is experienced more negatively than in-vehicle time in manually driven cars. This suggests that travellers do not perceive the theoretical advantage of being able to perform other tasks during the trip in an automated vehicle, at least not yet. Results also show that travellers’ attitudes regarding trust and sustainability of AVs are playing an important role in AVs attractiveness, which leads to uncertainty on how people will react when AVs are introduced in practice. We therefore state the importance of paying sufficient attention to these psychological factors, next to classic instrumental attributes like travel time and costs, before and during the implementation process of AVs as a public transport alternative. We recommend the extension of this research to revealed preference studies, thereby using the results of field studies.@en

Network risk assessment takes into consideration the probability that adverse events occur and the impacts of such disruptions on network functionality. In the context of transport networks, most studies have focused on vulnerability, the reduction in performance indicators given that a disruption occurs. This study presents and applies a method to explicitly account for exposure in identifying and evaluating link criticality in public transport networks. The proposed method is compared with conventional measures that lack exposure information. A criticality assessment is performed by accounting for the probability of a certain event occurring and the corresponding welfare loss. The methodology was applied for a multi-modal public transport network in the Netherlands where data concerning disruptions was available. The results expose the role of exposure in determining link criticality and overall network vulnerability. The findings demonstrate that disregarding exposure risks prioritizing links with high passenger volumes over links with a higher failure probability that are significantly more critical to network performance. The inclusion of exposure allows performing a risk analysis and has consequences on assessing mitigation measures and investment priorities.@en