Niels van Oort

Regularity analysis for optimizing urban transit network design

Transit network planners often propose network structures that either assume a certain level of regularity or are even especially focused on improving service reliability, such as networks in which parts of lines share a common route or the introduction of short-turn services. The key idea is that travelers on that route will have a more frequent transit service. The impact of such network designs on service regularity is rarely analyzed in a quantitative way. This paper presents a tool that can be used to assess the impact of network changes on the regularity on a transit route and on the level of transit demand. The tool can use actual data on the punctuality of the transit system. The application of such a tool is illustrated in two ways. A case study on introducing coordinated services shows that the use of such a tool leads to more realistic estimates than the traditional approach. Second, a set of graphs is developed which can be used for a quick scan when considering network changes. These graphs can be used to assess the effect of coordinating the schedules and of improving the punctuality.

Reliability Improvement in Short Headway Transit Services Schedule- and Headway-Based Holding Strategies

Improving service reliability is becoming a key focus for most public transport operators. One common operational strategy is holding. Holding vehicles can improve reliability, resulting in shorter travel times and less crowding. In this paper both schedule-based and headway-based holding strategies in short headway services are analyzed. Despite significant attention to holding in the current literature, some important aspects were not previously researched. The main new variables are maximum holding time, reliability buffer time, and, in the case of schedule-based holding, percentile value used to design the schedule. A real line in the Hague (Tram Line 9), Netherlands, and hypothetical lines are analyzed with various levels of running time variability. Headway-based and schedule-based holding have the largest effect if deviations are high. When schedule-based holding is applied with a maximum of 60-s holding time, the optimal value of the percentile value becomes about 65% for all lines analyzed. When no maximum holding time is applied, schedule-based holding is more effective; there is no difference when the maximum holding time is set to 60 s. This research also shows the effect of holding on crowding: an average level of irregularity of 20% could decrease to 15%, enabling either smaller capacity slack or less crowding.

Impact of Rail Terminal Design on Transit Service Reliability

Ensuring reliable rail transit services is an important task for transit agencies. The effects of various terminal configurations on reliability of services were studied. The results could also be used for short-turning infrastructure. Short turning is a widespread measure to restore service after major disturbances; in many rail networks, additional switches are constructed to enable short turning. Calculations of the average delay per vehicle, regarding three main types of terminals, show the effect of frequency and occupancy time [determined by the distance from the switches to the platform (i.e., length of the terminal), technical turning time, and scheduled layover time]. The substantial effect of arrival variability and the number of lines using the terminal are also illustrated. With stochastic variables, delays will occur, although they are not to be expected in the static case. The best performance regarding reliability is achieved when double crossovers are situated after the platforms. Single tail tracks facilitating the turning process are acceptable only if frequencies are low, although they are often used in practice as short-tuning facilities for high frequency services. Occupancy time has a large impact on expected delays. This time can be minimized by designing short distances between switches and platform and tail tracks. Capacity management is not common in transit. However, increasing frequencies and large deviations force the consideration of limited capacity when planning infrastructure. If not, delays will occur, and additional measures will be necessary to solve them, which could be more expensive in the long term.Ensuring reliable rail transit services is an important task for transit agencies. The effects of various terminal configurations on reliability of services were studied. The results could also be used for short-turning infrastructure. Short turning is a widespread measure to restore service after major disturbances; in many rail networks, additional switches are constructed to enable short turning. Calculations of the average delay per vehicle, regarding three main types of terminals, show the effect of frequency and occupancy time [determined by the distance from the switches to the platform (i.e., length of the terminal), technical turning time, and scheduled layover time]. The substantial effect of arrival variability and the number of lines using the terminal are also illustrated. With stochastic variables, delays will occur, although they are not to be expected in the static case. The best performance regarding reliability is achieved when double crossovers are situated after the platforms. Single t...

Line Length Versus Operational Reliability: Network Design Dilemma in Urban Public Transportation

The unreliability of public transportation is a well-known problem. During the design stages of public transportation, little attention is paid to operational reliability, although many design choices have a great impact on schedule adherence. During network design, operational reliability should be taken into account as a design parameter. This paper deals with line length. A new design dilemma is introduced: the length of the line versus operational reliability. Long lines offer many direct connections, thereby lowering the need for transfers. However, the variability is often negatively related to the length of a line and leads to less adherence to the schedule and additional waiting time for passengers. This paper suggests that both the positive and the negative effects of extending or connecting a line be taken into account. A tool that can be used to calculate the additional waiting time because of variability and transfers and that is based on actual journey and passenger data was developed. A case study in The Hague, Netherlands, shows that in the case of long lines with large variability, splitting of the line could result in less additional travel time because of improved operational reliability. This benefit compensates for the additional transfer time, provided that the transfer point is well chosen. This research shows the effect of choosing the transfer point at stops with many and fewer passing travelers. The latter could lead to a decrease in additional waiting time of about 30%. The splitting of a long line into two lines with an overlap in the central part could result in even more time savings. In that case, fewer travelers must transfer.The unreliability of public transportation is a well-known problem. During the design stages of public transportation, little attention is paid to operational reliability, although many design choices have a great impact on schedule adherence. During network design, operational reliability should be taken into account as a design parameter. This paper deals with line length. A new design dilemma is introduced: the length of the line versus operational reliability. Long lines offer many direct connections, thereby lowering the need for transfers. However, the variability is often negatively related to the length of a line and leads to less adherence to the schedule and additional waiting time for passengers. This paper suggests that both the positive and the negative effects of extending or connecting a line be taken into account. A tool that can be used to calculate the additional waiting time because of variability and transfers and that is based on actual journey and passenger data was developed. A case study in The Hague, Netherlands, shows that in the case of long lines with large variability, splitting of the line could result in less additional travel time because of improved operational reliability. This benefit compensates for the additional transfer time, provided that the transfer point is well chosen. This research shows the effect of choosing the transfer point at stops with many and fewer passing travelers. The latter could lead to a decrease in additional waiting time of about 30%. The splitting of a long line into two lines with an overlap in the central part could result in even more time savings. In that case, fewer travelers must transfer.

Control of Public Transportation Operations to Improve Reliability: Theory and Practice

RandstadRail is a new light rail system between the cities of The Hague, Rotterdam, and Zoetermeer in the Netherlands. During peak hours, the frequency on some trajectories is about 24 vehicles an hour. To deal with these high frequencies and to offer travelers a high-quality product according to the waiting times and the probability of getting a seat, the operator designed a three-step philosophy for controlling the system. The first step is to prevent deviations from occurring: the infrastructure is exclusively right-of-way as much as possible, and at intersections RandstadRail gets priority over the other traffic. RandstadRail stops at every stop and never leaves before the scheduled time. The second step in the philosophy is to deal with deviations by planning additional time in the schedule at stops, trajectories, and terminals. Small deviations can be solved in this way. The final step is to get vehicles back on schedule and is performed by the traffic control center: it has a total overview of all vehicles and can respond to disturbances, such as by slowing down vehicles near a delayed vehicle. Major disturbances may be experienced as a result of the rerouting and shortening of lines. RandstadRail has been in operation since 2007. The actual data on its performance were used to analyze the actual effects of the control philosophy. It is shown that because of the measures applied, the variability in trip times has been reduced, while punctuality has increased. This leads to a higher level of service, creating shorter trip times and a better distribution of passengers among the vehicles.RandstadRail is a new light rail system between the cities of The Hague, Rotterdam, and Zoetermeer in the Netherlands. During peak hours, the frequency on some trajectories is about 24 vehicles an hour. To deal with these high frequencies and to offer travelers a high-quality product according to the waiting times and the probability of getting a seat, the operator designed a three-step philosophy for controlling the system. The first step is to prevent deviations from occurring: the infrastructure is exclusively right-of-way as much as possible, and at intersections RandstadRail gets priority over the other traffic. RandstadRail stops at every stop and never leaves before the scheduled time. The second step in the philosophy is to deal with deviations by planning additional time in the schedule at stops, trajectories, and terminals. Small deviations can be solved in this way. The final step is to get vehicles back on schedule and is performed by the traffic control center: it has a total overview of all vehicles and can respond to disturbances, such as by slowing down vehicles near a delayed vehicle. Major disturbances may be experienced as a result of the rerouting and shortening of lines. RandstadRail has been in operation since 2007. The actual data on its performance were used to analyze the actual effects of the control philosophy. It is shown that because of the measures applied, the variability in trip times has been reduced, while punctuality has increased. This leads to a higher level of service, creating shorter trip times and a better distribution of passengers among the vehicles.

Short-Term Prediction of Ridership on Public Transport with Smart Card Data

Public transport operators are collecting massive amounts of data from smart card systems. In the Netherlands, every passenger checks in and checks out; this system creates detailed records of demand patterns. In buses and trams, users check in and check out in the vehicle; this factor provides good information on route choice. Options for analyzing smart card data and performing what-if analyses with transport planning software were explored. On the basis of big data, this new generation of transport demand models added to the existing range of transport demand models and approaches. The goal was to provide public transport operators with a simple (easy-to-build) model to perform what-if analyses. The data were converted to passengers per line and an origin–destination matrix between stops. This matrix was assigned to the network to repro-duce the measured passenger flows, and then what-if analysis became possible. With fixed demand, line changes could be investigated. With the introduction of an elastic demand model, changes in the level of service realistically affected passenger numbers. This method was applied to a case study in The Hague, Netherlands. Smart card data were imported into a transport model and connected with the network. The tool proved to be valuable to operators, who gained insights into the effects of small changes.

Improving Public Transport Decision Making, Planning and Operations by Using Big Data: Cases from Sweden and the Netherlands

New big data (sources) in the public transport industry enable to deal with major challenges such as elevating efficiency, increasing passenger ridership and satisfaction and facilitate the information flow between service providers and service users. This paper presents two actual cases from the Netherlands and Sweden in which automated data sources were utilized to support the planning and operational processes. The cases illustrate the benefits of using smartcard and vehicle positioning data. Due to the data (processing), valuable insights were gained helping to make the right choices and improve the public transport system.

Modelling multimodal transit networks integration of bus networks with walking and cycling

Demand for (public) transportation is subject to dynamics affected by technological, spatial, societal and demographic aspects. The political environment, together with financial and spatial constraints limit the possibilities to address transit issues arising from growing demand through the construction of new infrastructure. Upgrading of existing services and improving integration over the entire trip chain are two options that can address these transport issues. However, transport planners and transport service operators often fail to include the entire trip when improving services, as improvement is normally achieved through the adaptations of characteristics (e.g. speeds, stop distances) of the services. Our developed framework consists of two parts: one to assess the characteristics of the different bus services and their access and egress modes, and one to assess the effects of integration of these services, which includes the modelling and analysis in a regional transit model. The framework has successfully been applied to a case study showing that bus systems with higher frequencies and speeds can attract twice the amount of cyclists on the access and egress sides. It also shows that passengers accept longer access and egress distances with more positive characteristics of the bus service (higher speeds, higher frequencies).

Investigating Potential Transit Ridership by Fusing Smartcard and Global System for Mobile Communications Data

The public transport industry faces challenges in catering to the variety of mobility patterns and corresponding needs and preferences of passengers. Travel habit surveys provide information on overall travel demand as well as its spatial variation. However, that information often does not include information on temporal variations. By applying data fusion to smartcard and Global System for Mobile Communications (GSM) data, researchers were able to examine spatial and temporal patterns of public transport usage versus overall travel demand. The analysis was performed by contrasting different spatial and temporal levels of smartcard and GSM data. The methodology was applied to a case study in Rotterdam, Netherlands, to analyze whether the current service span is adequate. The results suggested that there is potential demand for extending public transport service on both ends. In the early mornings, right before transit operations are resumed, an hourly increase in visitor occupancy of 33% to 88% was observed in several zones, showing potential demand for additional public transport services. The proposed data fusion method was shown to be valuable in supporting tactical transit planning and decision making and can easily be applied to other origin-destination transport data.

Assessing and Improving Operational Strategies for the Benefit of Passengers in Rail-Bound Urban Transport Systems

Unplanned disruptions in transit can have consequent impacts on passengers. The more inconvenienced passengers are, the more likely operators will be negatively impacted. Yet so far, operators and researchers have addressed the rescheduling problem during disruptions mainly with a supply-side focus – timetable, crews, and vehicles – and not with a passenger perspective. Urban rail transit particularly lacks insights in terms of passenger-focused rescheduling. Being able to assess the inconvenience experienced by passengers during disruptions compared with what they normally experience, and being able to compare how different rescheduling strategies affect them are therefore two major challenges. The framework developed in this study precisely aims at tackling these challenges. A case study of the Rotterdam Metro is used to test the framework developed in this paper. Alternative strategies are developed focusing on the incident phase (from the beginning of the incident until its cause is resolved). The application of the framework reveals that a regularity-focused rescheduling strategy would be beneficial for high-frequency service users. Realistically, yearly savings could amount to around €900,000 in terms of societal passenger costs for the operator in the Rotterdam area alone. However, the omnipresence of the punctuality paradigm, through which most operators plan and analyze operations, makes the implementation of passenger-focused strategies a challenging task for traffic controllers. The results of the study are valuable for transit operators worldwide, and the framework could provide decision makers with insights on the performance of different strategies, bringing to light trade-offs between the supply and passenger sides during disruptions.