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•Naturalistic observations of CB operation status before and after COVID-19.•Exploration of influential factors affecting the in-practice decision-making process.•Consistent findings from discrete choice models and machine learning models.

Considering the inefficient resource allocation (RA) and high quality of service (QoS) requirement in vehicular communications, this paper proposes two dynamic RA algorithms, spectral clustering based greedy (SCGR) algorithm and multi-agent deep reinforcement learning (DRL) algorithm, to maximize both the sum capacity of the vehicle-to-infrastructure (V2I) uplinks and the total energy efficiency (EE) of the vehicle-to-vehicle (V2V) links by assigning the proper power and resource block (RB) to each V2V link in spatial modulation (SM) multiple-input multiple-output (SM-MIMO) vehicular system. For the SCGR algorithm, the spectral clustering (SC) scheme is first utilized to group the V2V links for the suitable RBs. Then, the optimal power is distributed to each V2V link by the greedy (GR) algorithm. For the DRL algorithm, a decentralized model-free network, improved multi-agent deep Q fully connected neural network (IDQFN), is developed to simultaneously find the best power allocation (PA) and RB assignment (RBA). Moreover, the SM technology is exploited to convey the information through the V2I and V2V links and improve the system capacity. Numerical results reveal that the proposed SCGR and IDQFN RA schemes outperform the typical RA algorithms, and the IDQFN scheme achieves better EE than the SCGR scheme, while the SCGR algorithm obtains the optimal average bit error rate (ABER) performance.

Potential fields have been integrated with local path-planning algorithms for autonomous vehicles (AVs) to tackle challenging scenarios with dense and dynamic obstacles. Most existing potential fields are isotropic without considering the traffic agent’s geometric shape and could cause failures due to local minima. We propose a three-dimensional potential field (TriPField) model to overcome this drawback by integrating an ellipsoid potential field with a Gaussian velocity field (GVF). Specifically, we model the surrounding vehicles as ellipsoids in corresponding ellipsoidal coordinates, where the formulated Laplace equation is solved with boundary conditions. Meanwhile, we develop a nonparametric GVF to capture the multi-vehicle interactions and then plan the AV’s velocity profiles, reducing the path search space and improving computing efficiency. Finally, a local path-planning framework with our TriPField is developed by integrating model predictive control to consider the constraints of vehicle kinematics. Our proposed approach is verified in three typical scenarios, i.e., active lane change, on-ramp merging, and car following. Experimental results show that our TriPField-based planner obtains a shorter, smoother local path with a slight jerk during control, especially in the scenarios with dense traffic flow, compared with traditional potential field-based planners. Our proposed TriPField-based planner can perform emergent obstacle avoidance for AVs with a high success rate even when the surrounding vehicles behave abnormally.

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.

The Modular Autonomous Vehicle (MAV) systems allow a vehicle module to join onto and detach from other modules to dynamically adjust vehicle capacity. It potentially renders transit agencies more flexibility to deal with the temporal fluctuations of passenger demand. In this work, we propose a strategy for flexible MAV scheduling on transit routes to meet the time-varying passenger demand. The proposed strategy is formulated as a bi-objective optimization model considering both the utilization of vehicles and service quality. The model determines the scheduled departure times from the terminals, the length of MAV for each scheduled trip, and the assignment of modules to all scheduled trips, simultaneously. The ε-constraint method is adopted to solve the developed model and the fuzzy satisfying approach is employed to select the best possible solution. We implement the proposed strategy in a real-world case study in comparison with a traditional strategy to demonstrate the effectiveness of the proposed strategy. The results show that the proposed strategy can remarkably improve the utilization of vehicles and also make passengers more convenient. Specifically, it leads to an 84.9% reduction in the total empty seat, as well as a 12.62% reduction in the total passenger waiting time.

The COVID-19 pandemic and subsequent governmental responses have had unprecedented effects on public transit (PT) demand. This paper presents a naturalistic observation of the Jiading bus transit system in Shanghai, China, spanning from April 2021 to October 2023 and covering different stages under various extreme policy response combinations. We employ Prais-Winsten regression to quantitatively assess the pandemic’s impact on bus demand and explore demand recovery patterns at both aggregated and individual levels in the post-pandemic era. Our findings reveal a strong negative correlation between bus demand and the stringency of containment policies, consistent across both levels of analysis. In the post-pandemic period, ridership has only rebounded to 77% of the pre-Omicron near-normal level, with notable spatial and temporal disparities across different regions. While the temporal distribution of ridership has largely normalized, the recovery of travel demand between zones outpaces that of travel within zones. Moreover, a persistent decline in individual travel frequency has been observed, which has not reverted in the post-pandemic period. The insights from this study can help policymakers better respond to potential future crises and improve PT services in the post-pandemic era.

Owing to the characteristics of spatial modulation (SM) and orthogonal time frequency space (OTFS) modulation, SM based OTFS (SM-OTFS) system has an excellent capability to improve the effectiveness and reliability of vehicle-to-vehicle (V2V) communication. To further combat the severe shadow effect, improve the system performance, and extend the transmission range of V2V communication in the urban scenario with dense buildings, we propose a cooperative multi-relay SM-OTFS system with the optimal relay selection in this paper, where both amplify-and-forward (AF) and decode-and-forward (DF) protocols have been employed and only the optimal relay participates in the signal forwarding. Moreover, the fast time-selective fading is mainly considered, the closed-form outage probability and the system capacity of the cooperative SM-OTFS (CSM-OTFS) system are derived from the viewpoint of signal transmissions in the time domain. Numerical results verify the superiority of the proposed CSM-OTFS system over the non-cooperative SM-OTFS system and illustrate the influence of the main system parameters on the system outage probability and capacity, such as the relaying protocol, the number of relays, the estimation rate of channel state information (CSI), and the time-varying channel characteristic.

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.

We propose statistical methodologies to investigate taxi operations at the airport. Based on Automatic Vehicle Location (AVL) data of nearly 9,000 taxis in five weekdays, we apply the proposed methodologies to evaluate taxi operations at Shanghai's Hongqiao international airport. The empirical studies statistically compare the operations of taxis serving the airport and urban areas and identify the periods in which the taxi profits are relatively stable. Possible explanations for the empirical results are provided. The findings would offer a better understanding of the mechanism that drives vacant-taxi drivers to serve the airport.

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.