Waiting Time Of Passengers Research Articles

Simulation analysis of dual-end queuing ride-hailing system considering driver-side queue management

Ride-hailing services have transformed urban transportation through convenience yet introduced new complexities around efficiency and traffic management. This study investigates the dual-queuing problem in ride-hailing from the driver perspective using a multi-agent simulation approach. The focus is dissecting the dynamics between driver search times and passenger wait times, which critically influence operational efficiency especially during peak demand. Exploring these interactions aims to uncover insights that could improve service efficiency and customer satisfaction. Addressing such ride-hailing challenges is vital not just for individual providers but also for advancing sustainable mobility across rapidly growing metropolitan regions. Enhanced efficiency connects to broader urban development narratives around livability, accessibility, and responsible mobility ecosystems.

Considering the Optimization Design of Urban Bus Network Scheduling

At present, the optimization of public transportation networks and vehicle scheduling are carried out independently in stages. However, through analysis, it has been found that scheduling information such as route schedules is an important factor related to passenger route selection. Therefore, in order to further improve the optimization effect, this article proposes an innovative idea of simultaneously optimizing the line network and scheduling. Based on the construction of a real–virtual public transportation network, this article constructs a synchronous optimization model for the line network and scheduling by considering both passenger waiting and on-board time. To achieve the consideration of passengers for different route choices, a shortest path traversal algorithm based on Yen was proposed to analyze the number and weight of the shortest paths between the same OD, and a genetic algorithm was used to solve the model. Finally, the effectiveness of the model was verified through numerical examples, and the results showed that synchronous optimization was superior to phased optimization: the passenger time cost was reduced by 21.5%, the bus operation cost was reduced by 13.7%, and the total bus system cost was reduced by 18.0%.

Comparison of passenger wait and travel time perceptions at bus stops

Passenger's perceived time is important in improving the public transport system. Since it is only possible to reach these values with a survey, it is very difficult to obtain them. Therefore, studies on this subject are very few and valuable. In the study, passengers' perceived waiting time and travel time values were determined at bus stop. In order to obtain these values, a survey was conducted with the passengers. The linear relationship between waiting time and travel time data was examined and a model was created. The data obtained with the basic regression model, which is the basis of machine learning, is divided into 20% test and 80% training data. The estimation was made with 85% success with the tested data.

Schedule-based transit assignment with online bus arrival information

Most public transportation services deviate from their published schedule. To cope with the delay caused by the unreliable service, passengers use online information about the bus arrival time which affects their route choice behavior. Current schedule-based transit assignment models fail to capture the passengers’ adaptive response to unreliable service, resulting in an inaccurate estimation of passenger wait time and passenger loads on various transit routes. The current study proposes schedule-based transit assignment models that incorporate online bus arrival information when modeling the passenger route choice in a stochastic and time-dependent transit network. The authors propose that passengers employ strategies when traveling between different origin–destination pairs not only due to the limited capacity of vehicles but also to cope with the transit delay. The passenger routing problem is modeled as a Markov Decision Process, and efficient algorithms are developed to solve this problem. Depending on the vehicle capacity, two types of assignment models are presented, namely, uncapacitated and capacitated assignments. When penalties for arriving at the destination outside the desired arrival time window are not applied, the uncapacitated assignment problem is formulated as a linear program. On the other hand, the capacitated assignment is formulated as a variational inequality problem for which an efficient Method of Successive Averages-based heuristic solution algorithm is proposed. Computational experiments are presented for a small and a large schedule-based transit network. The results show that denied boarding in an unreliable network leads to higher expected costs to passengers compared to the reliable and uncongested network. Furthermore, the analysis shows that the strategies evaluated with reliable schedule assumption lead to unreliable paths in the network and produces more transferring flow than should happen in practice. The application of our method to a subnetwork of the Twin Cities transit network with artificial demand reveals that passengers traveling from a residential area to the University of Minnesota campus may prefer taking a path with transfer in the event of highly unreliable transit service on the direct routes.

Curb Allocation and Pick-Up Drop-Off Aggregation for a Shared Autonomous Vehicle Fleet

Advances in information technologies and vehicle automation have birthed new transportation services, including shared autonomous vehicles (SAVs). Shared autonomous vehicles are on-demand self-driving taxis, with flexible routes and schedules, able to replace personal vehicles for many trips in the near future. The siting and density of pick-up and drop-off (PUDO) points for SAVs, much like bus stops, can be key in planning SAV fleet operations, since PUDOs impact SAV demand, route choices, passenger wait times, and network congestion. Unlike traditional human-driven taxis and ride-hailing vehicles like Lyft and Uber, SAVs are unlikely to engage in quasi-legal procedures, like double parking or fire hydrant pick-ups. In congested settings, like central business districts (CBD) or airport curbs, SAVs and others will not be allowed to pick up and drop off passengers wherever they like. This paper uses an agent-based simulation to model the impact of different PUDO locations and densities in the Austin, Texas CBD, where land values are highest and curb spaces are coveted. In this paper 18 scenarios were tested, varying PUDO density, fleet size and fare price. The results show that for a given fare price and fleet size, PUDO spacing (e.g., one block vs. three blocks) has significant impact on ridership, vehicle-miles travelled, vehicle occupancy, and revenue. A good fleet size to serve the region’s 80 core square miles is 4000 SAVs, charging a $1 fare per mile of travel distance, and with PUDOs spaced three blocks of distance apart from each other in the CBD.

A Multi-Period Model for Optimal Changi Airport Check-In Counter Operations

Growth in air passenger flow has caused severe congestion at the airport check-in counter, posing a significant problem for airport management. Particularly during the check-in process, the necessary authorities must coordinate sufficient facilities with adequate staffing levels. The airport check-in counter problem (ACCAP) is a field concerned with establishing the optimal number of check-in counters to balance operating expenses and passenger wait times in order to reduce airport congestion. Expanding the number of counters and staff to a minimum operating cost is able to prevent the congestion problem from escalating without incurring further operating expenses. This paper focused on proposing optimal scheduling of airport check-in counters operations, including staffing. A dynamic model with multi-period principles is adapted to address the aforementioned problem by balancing the trade-off between service performance and operational cost. As a case study, data from Singapore Changi International Airport was utilized. The findings are also discussed in terms of the flow of passengers throughout the airport check-in procedure and operations. As a result, the number of activated counters is minimized throughout all shifts by applying the dynamic model at the average service time. At the same time, there are fewer passengers in the queue.

Robust design for underground metro systems with modular vehicles

The asymmetric demands of metro lines in megacities can cause high passenger wait times and substantial underutilization of vehicle capacity. The problem is difficult to address because of passenger flow uncertainties and random delays. We propose a modular transit system (MTS) that allows a metro fleet to be dynamically dissembled and assembled in identical modules (or carriages) on metro terminals. A formal formulation of this issue is provided with a nonlinear programming (NLP) model that considers train power, greenhouse gas emissions, wind resistance, and operational economics. Then, a linearization of the NLP further facilitates its fast solution. By utilizing numerical experiments based on Shenzhen Metro data, we illustrate the mathematical model’s viability and confirm the model's usefulness in terms of the economic, low-carbon, and ecological consequences. Then, the robustness of the proposed model and the sensitivity analysis with various parameter values are reported.

Semi-autonomous bus platooning service optimization with surrogate modeling

Autonomous driving holds great promise for easing the driver shortage issue that has occurred in many countries. In this paper, we consider a semi-autonomous transit system where (1) individual buses could form platoons to increase bus capacity during peak hours and operate separately during off-peak hours; and (2) buses may drive autonomously in certain geofenced areas yet need to be guided by human drivers elsewhere (e.g., SAE level 4, semi-autonomous). We model buses driving autonomously within the geofenced area as short-turn bus service, and jointly optimize bus dispatch headway, platoon size and service type through an integer programming model to address the driver shortage issue. The proposed model considers real-world and micro-level bus operation processes for mixed traffic of short-turn and full route buses, with bus fleet and driver workforce constraints being modeled endogenously. Since the model is non-linear, we further develop a radial basis function-based surrogate framework to solve the model efficiently. Experimental results show that, compared to the conventional bus service that uses fixed bus capacity only, semi-autonomous bus platooning service reduces operation cost significantly while reducing passenger wait time. System performance variations of the proposed model under different driver availabilities are also examined.

Optimization of On-Demand Shared Autonomous Vehicle Deployments Utilizing Reinforcement Learning.

Ride-hailed shared autonomous vehicles (SAV) have emerged recently as an economically feasible way of introducing autonomous driving technologies while serving the mobility needs of under-served communities. There has also been corresponding research work on optimization of the operation of these SAVs. However, the current state-of-the-art research in this area treats very simple networks, neglecting the effect of a realistic other traffic representation, and is not useful for planning deployments of SAV service. In contrast, this paper utilizes a recent autonomous shuttle deployment site in Columbus, Ohio, as a basis for mobility studies and the optimization of SAV fleet deployment. Furthermore, this paper creates an SAV dispatcher based on reinforcement learning (RL) to minimize passenger wait time and to maximize the number of passengers served. The created taxi-dispatcher is then simulated in a realistic scenario while avoiding generalization or over-fitting to the area. It is found that an RL-aided taxi dispatcher algorithm can greatly improve the performance of a deployment of SAVs by increasing the overall number of trips completed and passengers served while decreasing the wait time for passengers.

Passenger Wait Time Perceptions at Bus Stops: Empirical Results and Impact on Evaluating Real-Time Bus Arrival Information

This study quantifies the relationship between perceived and actual waiting times experienced by passengers awaiting the arrival of a bus at a bus stop. Understanding such a relationship would be useful in quantifying the value of providing real-time information to passengers on the time until the next bus is expected to arrive at a bus stop. Data on perceived and actual passenger waiting times, along with socioeconomic characteristics, were collected at bus stops where no real-time bus arrival information is provided, and relationships between perceived and actual waiting times are estimated. The results indicate that passengers do perceive time to be greater than the actual amount of time waited. However, the hypothesis that the rate of change of perceived time does not vary with respect to the actual waiting time could not be rejected (over a range of 3 to 15 minutes). Assuming that a passenger’s perceived waiting time is equal to the actual time when presented with accurate real-time bus arrival information, the value of the eliminated additional time is assessed in the form of reduced vehicle hours per day resulting from a longer headway that produces the same mean passenger waiting time. The eliminated additional time is also assessed in the form of uncertainty in the headway resulting in the same extra waiting time. Naturally, such benefits of passenger information can only be confirmed when the actual effect of information on the perception of waiting time is quantified.

An Analysis of Commuter Rail Real-Time Information in Boston

Prior studies have assessed the impacts of real-time information (RTI) provided to bus and heavy rail riders but not commuter rail passengers. The objective of this research is to investigate the benefits of providing commuter rail RTI. The method is a three-part statistical analysis using data from an on-board survey on two commuter rail lines in the Boston region. The first analysis assesses overarching adoption, and the results show that one-third of commuter rail riders use RTI. The second part conducts difference of means tests and regression analysis on passenger wait times, which reveals that riders’ use of RTI is correlated with a decrease in self-reported “usual” wait times. The third part analyzes 12 quality-of-service indicators, which have a limited relationship with RTI utilization. The results suggest that the benefits of commuter rail RTI are modest. Despite this, many commuter rail riders choose to use this new information source, which has important implications for transit managers considering deploying RTI systems.

Research on commuters’ carpooling behavior in the mobile internet context

The real-time sharing of information through mobile internet facilitates and increases demand for carpool commuting. In this study, information asymmetries are transformed into psychological costs to passengers and drivers; then, the psychological and other physical costs are integrated into a generalized travel cost. Logit and Nested logit models are used to analyze commuters' mode choices and to determine the densities of carpooling demand. The success rates of carpools across different demand densities are estimated using cooperative game theory, and carpool trips, driver pick-up distances, passenger wait times, and changes in the modal splits of commutes are calculated. Ningbo City, China is taken as an example for numerical analysis. Based on data from our survey and orders from more than ten OCH platforms, the growth trend of carpooling can be forecast., The results show that, due to mobile internet, the probabilities that drivers and passengers are willing to carpool increases from 19% and 15% to 28% and 20%, respectively. Simultaneously, the average pick-up distance of drivers declines to 2.9 km, and passengers' average wait time to 9 min and 24 s. The actual mode shares of carpooling for drivers and passengers increase to 20% and 17%, respectively. Carpool commuting thus exhibits a significant potential for growth through mobile internet.

ASPIRES: Airport Shuttle Planning and Improved Routing Event-driven Simulation

Most of the existing traffic simulation packages require significant calibration work to be able to reflect reality. To evaluate special operations including emerging technologies, a microscopic simulation that tracks detailed interactions of all the elements of the traffic systems is usually needed. This type of simulation is usually computationally demanding. This work developed an Airport Shuttle Planning and Improved Routing Event-driven Simulation (ASPIRES) package to simulate and evaluate current, potential, and future airport shuttle operations. The simulation was driven by data and thus did not require much calibration effort. The discrete-event simulation nature of ASPIRES makes the simulation computationally efficient. Simulating 1 day of shuttle operations takes less than 2 s. The study site of this work is the Dallas/Fort Worth International Airport in the U.S. The shuttle service that connects the five terminals of the airport and the rental car center was studied. Travel times, dwell times, and passenger arrivals were simulated using empirical distributions derived mainly from real data to capture the stochastic nature of the rental car center shuttle bus operations. Data on bus miles traveled, bus energy consumption, passenger wait times, and passengers left behind at stops were collected to study the trade-off between energy use and passenger experience. Electric bus and on-demand bus operations were also included. The simulation outputs can show passengers statistics at terminals, shuttles statistics, and charging station statistics. ASPIRES cannot be used to model a generic traffic system but is well-suited for fleet systems.

Adaptive transit scheduling to reduce rider vulnerability during heatwaves

ABSTRACT Extreme heat events induced by climate change present a growing risk to transit passenger comfort and health. To reduce exposure, agencies may consider changes to schedules that reduce headways on heavily trafficked bus routes serving vulnerable populations. This paper develops a schedule optimization model to minimize heat exposure and applies it to local bus services in Phoenix, Arizona, using agent-based simulation to inform travel demand and rider characteristics. Rerouting as little as 10% of a fleet is found to reduce network-wide exposure by as much as 35% when operating at maximum fleet capacity. Outcome improvements are notably characterized by diminishing returns, owing to skewed ridership and the inverse relationship between fleet size and passenger wait time. Access to spare vehicles can also ensure significant reductions in exposure, especially under the most extreme temperatures. Rerouting, therefore, presents a low-cost, adaptable resilience strategy to protect riders from extreme heat exposure.

"Internet +" Configuration Taxi Resource Era

This paper mainly studies the problem of the difficulty of taking a taxi, first of all, the degree of matching supply and demand is affected by taxis and passengers, so the indicators can be established from both drivers and passengers. The indicators we established are taxi idling rate, people ownership, passenger waiting time. Then, we take the maximum matching number between driver and passenger and the minimum total waiting time of passengers as the objective function to establish the maximum matching model between passenger and driver. The case analysis shows that this method can achieve better utilization of taxi resources and has strong operability.

Novel model for integrated demand-responsive transit service considering rail transit schedule.

This research aims to develop an optimization model for optimizing demand-responsive transit (DRT) services. These services can not only direct passengers to reach their nearest bus stops but also transport them to connecting stops on major transit systems at selected bus stops. The proposed methodology is characterized by service time windows and selected metro schedules when passengers place a personalized travel order. In addition, synchronous transfers between shuttles and feeder buses were fully considered regarding transit problems. Aiming at optimizing the total travel time of passengers, a mixed-integer linear programming model was established, which includes vehicle ride time from pickup locations to drop-off locations and passenger wait time during transfer travels. Since this model is commonly known as an NP-hard problem, a new two-stage heuristic using the ant colony algorithm (ACO) was developed in this study to efficiently achieve the meta-optimal solution of the model within a reasonable time. Furthermore, a case study in Chongqing, China, shows that compared with conventional models, the developed model was more efficient formaking passenger, route and operation plans, and it could reduce the total travel time of passengers.

Queue Management and Passenger Wait Time Perceptions at High-Volume Bus Stations

Queue Management and Passenger Wait Time Perceptions at High-Volume Bus Stations

To pool or not to pool: Equilibrium, pricing and regulation

We study a transportation network company (TNC) that offers on-demand solo and pooling e-hail services in an aggregate mobility service market, while competing with transit for passengers. The market equilibrium is established based on a spatial driver–passenger matching model that determines the passenger wait time for both solo and pooling rides. We prove, under mild conditions, this system always has an equilibrium solution. Built on the market equilibrium, three variants of pricing problems are analyzed and compared, namely, (i) profit maximization, (ii) profit maximization subject to regulatory constraints, and (iii) social welfare maximization subject to a revenue-neutral constraint. A comprehensive case study is constructed using TNC data collected in the city of Chicago. We found pooling is desirable when demand is high but supply is scarce. However, its benefit diminishes quickly as the average en-route detour time (i.e., the difference between the average duration of solo and pooling trips) increases. Without regulations, a mixed strategy—providing both solo and pooling rides—not only achieves the highest profit and trip production in most scenarios, but also gains higher social welfare. The minimum wage policy can improve social welfare in the short term. However, in the long run, the TNC could react by limiting the size of the driver pool, and consequently, render the policy counterproductive, even pushing social welfare below the unregulated level. Moreover, by maintaining the supply and demand of ride-hail at an artificially high level, the minimum wage policy tends to exacerbate traffic congestion by depressing the use of collective modes (transit and pooling). A congestion tax policy that penalizes solo rides promotes pooling, but may harm social welfare. However, it promises to increase both social welfare and pooling ratio when jointly implemented with the minimum wage policy.

Autonomous Bus Fleet Control Using Multiagent Reinforcement Learning

Autonomous buses are becoming increasingly popular and have been widely developed in many countries. However, autonomous buses must learn to navigate the city efficiently to be integrated into public transport systems. Efficient operation of these buses can be achieved by intelligent agents through reinforcement learning. In this study, we investigate the autonomous bus fleet control problem, which appears noisy to the agents owing to random arrivals and incomplete observation of the environment. We propose a multi-agent reinforcement learning method combined with an advanced policy gradient algorithm for this large-scale dynamic optimization problem. An agent-based simulation platform was developed to model the dynamic system of a fixed stop/station loop route, autonomous bus fleet, and passengers. This platform was also applied to assess the performance of the proposed algorithm. The experimental results indicate that the developed algorithm outperforms other reinforcement learning methods in the multi-agent domain. The simulation results also reveal the effectiveness of our proposed algorithm in outperforming the existing scheduled bus system in terms of the bus fleet size and passenger wait times for bus routes with comparatively lesser number of passengers.

Joint Rebalancing and Vehicle-to-Grid Coordination for Autonomous Vehicle Public Transportation System

An Autonomous Vehicle (AV) is believed to be the next generation transport that can enhance safety and efficiency for smart mobility. In an AV-based public transportation system, the full autonomy of AVs enables high-efficiency transport services and the potential ride-sharing feature of AV system enhances the utilization. The system manages a fleet of AVs, determines their assignments to transport requests, sends instructions concerning the optimized plans, and recommends the parking locations for the unoccupied AVs. The parking location of an empty AV is crucial in the sense that rebalancing AVs to areas with high potential service demand can curtail the unnecessary waiting time for passengers. As AVs are generally electric, proper parking locations can also facilitate vehicle-to-grid (V2G) support. In this paper, we propose a joint rebalancing and V2G coordination strategy for AV-based public transportation system. We formulate the problem as an integer linear program and propose a heuristic based on Genetic Algorithm and Model Predictive Control to solve the problem in low time complexity. Extensive experiments are performed with the real taxi service data from New York City. The formulated integer linear program is solved dynamically where each problem instance contains 3 to 5 AVs and 3 to 8 requests in 30s time interval. Compared with the transport system without rebalancing, the results show that the coordination strategy is efficient and effective in reducing unnecessary waiting time for passengers while satisfying V2G support. Compared to computational time with the standard solver, the proposed heuristic dramatically reduces the computational time.