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During a planned special event (PSE), vehicular and pedestrian movements are concentrated around the PSE venue for short periods, leading to potential conflicts and safety issues. Vehicles tend to park as close as possible to the venue for the convenience of attendees. Taking into account the characteristics of PSE traffic patterns, a strategic vehicle parking planning model is proposed to integrally optimize the assignment of parking lots and inbound/outbound routes around the venue. Two prioritized objectives were considered for traffic safety and for the convenience of attendees. The proposed model was first validated in a small-scale example, and then adopted in a real-world case that took place in Taiyuan, China. The resulting parking plan presented an efficient reduction in the conflict points between vehicular and pedestrian movements, leading to an acceptable average walking distance for attendees. To evaluate the impact of different management preferences on the parking plan, a series of weights for conflict points were set according to potential conflict locations, occurrence times, and types. The results demonstrated that the number of conflict points with higher weights could be effectively reduced by the parking plan.

A bi-level model was developed to design the short-turning strategy on a bus route. The upper-level model aimed at minimizing the total cost, including operational cost, passengers’ waiting time cost, and in-vehicle travel time cost. The lower-level model was a logit model to capture the service choices of passengers. The effects of bus crowding and seat availability were considered explicitly in the proposed model. An algorithm was developed to determine the frequencies of different services and the turnback points of the short-turning service. A case study demonstrates the superiority of the proposed model over alternative models. Sensitivity of the optimal design to seat capacity was also investigated.

A bi-level model was developed to design the short-turning strategy on a bus route. The upper-level model aimed at minimizing the total cost, including operational cost, passengers’ waiting time cost, and in-vehicle travel time cost. The lower-level model was a logit model to capture the service choices of passengers. The effects of bus crowding and seat availability were considered explicitly in the proposed model. An algorithm was developed to determine the frequencies of different services and the turnback points of the short-turning service. A case study demonstrates the superiority of the proposed model over alternative models. Sensitivity of the optimal design to seat capacity was also investigated.

Shanghai, China, has the largest metro system in the world, with a network length of more than 550 km. Both Shanghai and Beijing are among the top five cities in terms of ridership, and some of the most important components of their metro systems are the ring transit lines. Many other cities, in China and elsewhere, also envision a ring transit line for their future rail transit networks. A previously developed analytical model for the long-range planning of ring transit lines was used in the comparison of the current alignment of the Shanghai ring line with the optimized model output, and a second ring transit line was recommended for the future Shanghai network. The findings suggest that the alignment of an existing ring line would affect the optimal alignment of the second ring line. In addition, if an outer ring line exists (or is planned to be constructed), the optimal location of the inner ring line might not be its current location. Furthermore, a sensitivity analysis was conducted to test the impact of changes on demand, value of time, and passenger ride cost on the second ring line. Zones that would benefit most from introduction of the second ring line were also determined. Although the case study presents the Shanghai ring lines, the outcomes provide useful information for other cities that are considering the expansion of their transit network with a first or second ring line. Unlike simulations and agent-based models, the model presented in this study is easily transferable to any transit network.

Shanghai, China, has the largest metro system in the world, with a network length of more than 550 km. Both Shanghai and Beijing are among the top five cities in terms of ridership, and some of the most important components of their metro systems are the ring transit lines. Many other cities, in China and elsewhere, also envision a ring transit line for their future rail transit networks. A previously developed analytical model for the long-range planning of ring transit lines was used in the comparison of the current alignment of the Shanghai ring line with the optimized model output, and a second ring transit line was recommended for the future Shanghai network. The findings suggest that the alignment of an existing ring line would affect the optimal alignment of the second ring line. In addition, if an outer ring line exists (or is planned to be constructed), the optimal location of the inner ring line might not be its current location. Furthermore, a sensitivity analysis was conducted to test the impact of changes on demand, value of time, and passenger ride cost on the second ring line. Zones that would benefit most from introduction of the second ring line were also determined. Although the case study presents the Shanghai ring lines, the outcomes provide useful information for other cities that are considering the expansion of their transit network with a first or second ring line. Unlike simulations and agent-based models, the model presented in this study is easily transferable to any transit network.

Bus drivers have responded positively toward real-time bus information in various surveys. However, empirical studies on their actual responses are limited. On the basis of actual automatic vehicle location data, this study quantified bus drivers' responses to real-time schedule adherence and their effects on transit reliability. Bus trips that were ahead of and behind schedule were analyzed separately at timepoint stops, regular stops, and along the roadways between stops. Results revealed that bus drivers would use real-time information to keep on schedule. Early buses were found to be more likely to make adjustments in response to information than were late buses along the roadways. Moreover, bus drivers' responses to real-time information was found to improve transit reliability: 50% of the improvement was the result of drivers' responses to schedule adherence at timepoint stops and 50% was the result mainly of drivers' responses to schedule adherence along the roadways. The likelihood that drivers would make adjustments at regular stops to adhere to schedule was low.

Bus drivers have responded positively toward real-time bus information in various surveys. However, empirical studies on their actual responses are limited. On the basis of actual automatic vehicle location data, this study quantified bus drivers’ responses to real-time schedule adherence and their effects on transit reliability. Bus trips that were ahead of and behind schedule were analyzed separately at timepoint stops, regular stops, and along the roadways between stops. Results revealed that bus drivers would use real-time information to keep on schedule. Early buses were found to be more likely to make adjustments in response to information than were late buses along the roadways. Moreover, bus drivers’ responses to real-time information was found to improve transit reliability: 50% of the improvement was the result of drivers’ responses to schedule adherence at timepoint stops and 50% was the result mainly of drivers’ responses to schedule adherence along the roadways. The likelihood that drivers would make adjustments at regular stops to adhere to schedule was low.

This study proposed a robust dynamic control strategy to regulate bus headways and prevent buses from bunching by holding them at bus stops. The proposed strategy monitors bus locations in real time and estimates the time gaps between consecutive buses at a desired frequency. The holding times of all buses at their respective immediately downstream stops are determined simultaneously on the basis of the estimated time gaps. A procedure that consists of a discrete quadratic dynamic control program and a quadratic static optimization program was developed to produce a unique dynamic control law of holding times. Numerical investigations on an operational bus route revealed that the proposed strategy produced greater system reliability than did some existing control strategies and that the bus system under the control of the proposed strategy recovered promptly from large system disruptions.

This study proposed a robust dynamic control strategy to regulate bus headways and prevent buses from bunching by holding them at bus stops. The proposed strategy monitors bus locations in real time and estimates the time gaps between consecutive buses at a desired frequency. The holding times of all buses at their respective immediately downstream stops are determined simultaneously on the basis of the estimated time gaps. A procedure that consists of a discrete quadratic dynamic control program and a quadratic static optimization program was developed to produce a unique dynamic control law of holding times. Numerical investigations on an operational bus route revealed that the proposed strategy produced greater system reliability than did some existing control strategies and that the bus system under the control of the proposed strategy recovered promptly from large system disruptions.

Origin-destination (O-D) flows at the bus passenger route level have traditionally been estimated from costly and labor-intensive onboard surveys. The availability of automatic passenger counter (APC) data on many bus transit systems offers the possibility of enhancing the quality of the onboard survey data at little marginal cost. This paper investigates the value of estimating route-level passenger O-D flows from APC data and onboard O-D survey data with a focus on the effect of onboard O-D survey sample size. An empirical study using field data collected on three bus routes investigates and quantifies the value of combining APC counts with onboard O-D survey data as a function of survey sample size. Encouraging estimation performance obtained in using APC data with no onboard O-D survey data, previously seen in a smaller study, is confirmed in this more extensive study. In addition, incorporating onboard O-D survey data with APC data produces O-D flow estimates that are better than those produced by using only the APC data, and increasing the sample size of the onboard O-D survey improves the quality of these estimates. However, the magnitude of the improvement depends on the O-D flow structure of the given bus route; increasing sample size results in less appreciable improvement for routes with more concentrated O-D flows than for routes with more evenly distributed O-D flows.

Bus passenger origin-destination (O-D) flow matrices portray information on travel patterns that can be used for route planning, design, and operations functions. Because travel patterns are known to vary throughout the day, O-D flow matrices can be expected to vary throughout the day as well. A method identifies time-of-day periods of homogeneous normalized bus route O-D passenger flow matrices in which a normalized matrix depicts the probabilities that a random passenger in the homogeneous period will travel from various origin stops to various destination stops on the route. The method uses bus trip automatic passenger counter data to estimate trip-level O-D matrices, aggregates the trip-level O-D matrices into elemental matrices for relatively short time periods, and then considers these elemental matrices as inputs to a traditional clustering procedure that is modified to ensure that a cluster indicating a period of homogeneous normalized O-D flow spans a continuous time period during the day. The homogeneous O-D flow period method is applied to empirical automatic passenger counter data collected on a bus route for which temporal travel patterns are understood. The time periods identified correspond well to the a priori understanding of travel patterns. A parallel method that uses passenger volume, rather than estimated normalized O-D flow matrices, is applied to the same data. The periods identified by this volume-based approach are not responsive to the changes in the normalized O-D flow patterns determined by the homogeneous O-D flow period identification method.

Bus passenger origin-destination (O-D) flow matrices portray information on travel patterns that can be used for route planning, design, and operations functions. Because travel patterns are known to vary throughout the day, O-D flow matrices can be expected to vary throughout the day as well. A method identifies time-of-day periods of homogeneous normalized bus route O-D passenger flow matrices in which a normalized matrix depicts the probabilities that a random passenger in the homogeneous period will travel from various origin stops to various destination stops on the route. The method uses bus trip automatic passenger counter data to estimate trip-level O-D matrices, aggregates the trip-level O-D matrices into elemental matrices for relatively short time periods, and then considers these elemental matrices as inputs to a traditional clustering procedure that is modified to ensure that a cluster indicating a period of homogeneous normalized O-D flow spans a continuous time period during the day. The homogeneous O-D flow period method is applied to empirical automatic passenger counter data collected on a bus route for which temporal travel patterns are understood. The time periods identified correspond well to the a priori understanding of travel patterns. A parallel method that uses passenger volume, rather than estimated normalized O-D flow matrices, is applied to the same data. The periods identified by this volume-based approach are not responsive to the changes in the normalized O-D flow patterns determined by the homogeneous O-D flow period identification method.

Origin–destination (O-D) flows at the bus passenger route level have traditionally been estimated from costly and labor-intensive onboard surveys. The availability of automatic passenger counter (APC) data on many bus transit systems offers the possibility of enhancing the quality of the onboard survey data at little marginal cost. This paper investigates the value of estimating route-level passenger O-D flows from APC data and onboard O-D survey data with a focus on the effect of onboard O-D survey sample size. An empirical study using field data collected on three bus routes investigates and quantifies the value of combining APC counts with onboard O-D survey data as a function of survey sample size. Encouraging estimation performance obtained in using APC data with no onboard O-D survey data, previously seen in a smaller study, is confirmed in this more extensive study. In addition, incorporating onboard O-D survey data with APC data produces O-D flow estimates that are better than those produced by using only the APC data, and increasing the sample size of the onboard O-D survey improves the quality of these estimates. However, the magnitude of the improvement depends on the O-D flow structure of the given bus route; increasing sample size results in less appreciable improvement for routes with more concentrated O-D flows than for routes with more evenly distributed O-D flows.