Rob van Nes

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.

MULTIMODAL CHOICE SET COMPOSITION: ANALYSIS OF REPORTED AND GENERATED CHOICE SETS

Multimodal trips are common in todays travel and are expected to become more important. An individual embarking on a multimodal trip faces a number of choice dimensions, such as access and egress mode or modes, origin and destination railway stations, train service types, and transfer stations. For each of these choice dimensions, multiple alternatives are available. To gain insight into the structure and complexity of multimodal trips, a dedicated survey was conducted. Its results include detailed data on chosen trips and reported trip alternatives of 511 multi-modal home-bound trips in which the train is the main transport mode. Furthermore, objective choice sets were generated for these respondents and compared with the chosen trips and the reported trip alternatives. It can be concluded that many alternatives are available to travelers, whereas only a limited subset of those alternatives is actually perceived. Even fewer alternatives are actually considered in the choice process. The obtained knowledge of the chosen trips and subjective choice sets can be used to improve choice set generation algorithms and to define strategies for modeling route choice in multimodal networks.

Service Reliability in a Network Context Impacts of Synchronizing Schedules in Long Headway Services

This paper presents research on synchronization of transfers and its impact on service reliability from a passenger perspective. Passenger reliability was analyzed for the case of a multioperator transfer node. A method was developed to calculate the passenger-centered reliability indicators, additional travel time, and reliability buffer time, by using scheduled and actual vehicle arrival and departure times as inputs. Five major factors were identified as affecting reliability at a particular transfer: scheduled transfer time, distributions of actual arrivals of the first and second line, headways, transfer walking time, and transfer demand. The method was demonstrated in a real network case, in which changing a specific transfer had effects on other transfers from the transfer point. This method can be applied in a cost-benefit analysis to identify the benefits and costs of reliability for different groups of passengers, thereby supporting proper decision making.

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...

USING CHOICE SETS FOR ESTIMATION AND PREDICTION IN ROUTE CHOICE

This article investigates the relationships between choice set types and analysis purpose, especially estimation and prediction. The choice set types considered are generated objective choice sets and observed subjective choice sets. While for estimation purposes subjective choice sets might be preferred, although objective choice sets might have benefits as well, objective choice sets appear to be most suitable for prediction purposes. Empirical analysis of choice sets for multi-modal inter-urban train trips shows clear distinction between choice models estimated using objective choice sets and using subjective choice sets. Applying these choice models for prediction shows that choice models based on subjective choice sets have a poorer performance when applied to objective sets than vice versa. As a result it is recommended to use objective choice sets for both estimation and prediction purposes.

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.

Application of Constrained Enumeration Approach to Multimodal Choice Set Generation

Collected data often include only information about chosen routes. To gain insight into travelers’ route choice behavior or to predict route shares, one must know the set of alternatives from which travelers have chosen their routes. An alternative approach to choice set generation in mixed multimodal networks is presented. This new algorithm—a run-based, constrained enumeration method that uses branch-and-bound techniques—is suitable for both estimation and prediction. One key characteristic of the algorithm is a set of constraints that reflects observed travel behavior. The proposed algorithm for choice set generation can be applied to a complete multimodal network at once. However, by exploiting knowledge about the structure of multimodal trips, the separate application of the algorithm to partial networks and consecutive concatenation of subroutes into complete door-to-door routes substantially reduce computation times without resulting in incomplete choice sets. This algorithm for choice set generation has been calibrated for and successfully applied to a real-size, mixed multimodal transport network in the Netherlands. A comparison of generated choice sets with reported chosen and known alternatives indicated that the algorithm can generate these alternatives, with high coverage levels as a result. This result clearly indicates that this constrained enumeration approach meets the requirements for choice set generation and thus offers interesting perspectives for route choice analysis and the prediction of route shares. Furthermore, the separate application of the algorithm to partial networks and the consecutive concatenation of subroutes into complete door-to-door trips substantially do not result in incomplete choice sets.

Multiuser-Class Urban Transit Network Design

In transit network design it is common to use characteristics of the average traveler to describe travel behavior, while in reality different traveler groups can be distinguished that react differently with respect to transport service quality. A study is conducted of the possible consequences of basing the design of urban transit networks on the preferences of specific traveler groups. To that end, an analytical network optimization model is developed that considers a mix of different traveler groups simultaneously. Results from the analyses show that focusing on specific traveler groups leads to clearly different network design characteristics. However, the optimal network design developed for the average traveler proved to be the best network for all traveler groups. Furthermore, it was found that focusing on traveler groups having good transport alternatives led to very low values of consumer surplus and social welfare. Optimizing transit networks while considering different traveler groups simultaneously results in networks that are similar to those using the traditional single-user-class approach based on the average traveler. Differences in preferences for traveler groups are balanced by the size of the resulting transit patronage. Apparently, a more realistic description of the demand side is not essential for urban transit network design.