Vehicle Dwell Time Research Articles

Passenger‐based adaptive transit signal priority for BRT systems with multiple loading areas

AbstractAn adaptive transit signal priority strategy is presented in this paper with the objective of passenger delay minimization at isolated intersections serving conflicting bus rapid transit (BRT) routes. The proposed passenger‐based adaptive signal priority for BRT systems (PASPB) is designed to optimize both green times and phase sequences at the start of each cycle and for a prespecified decision horizon. Since a public transportation (PT) vehicle travel time model capable of estimating the dwell time at stops with multiple loading areas has not yet been developed, PT vehicle dwell time is modeled in this study by analyzing the cases of passenger service by single or double PT vehicles. The problem is formulated as a mixed‐integer nonlinear program (MINLP) and at each execution, the optimization is conducted by genetic algorithm. The model is deployed to a real‐field intersection with conflicting BRT routes under the SUMO microsimulation environment. The results show that PASPB outperforms the SYNCHRO optimal solution and phase insertion strategy regarding PT passenger delay. Besides, the sensitivity analysis proves that at high demand levels of the PT system or general traffic, PASPB presents the best performance in terms of general traffic, PT, and total passenger delay compared to other models.

Deep Reinforcement Q-Learning for Intelligent Traffic Control in Mass Transit

Traffic control in mass transit consists of the regulation of both vehicle dynamics and passenger flows. While most of the existing approaches focus on the optimization of vehicle dwell time, vehicle time headway, and passenger stocks, we propose in this article an approach which also includes the optimization of the passenger inflows to the platforms. We developed in this work a deep reinforcement Q-learning model for the traffic control in a mass transit line. We first propose a new mathematical traffic model for the train and passengers dynamics. The model combines a discrete-event description of the vehicle dynamics, with a macroscopic model for the passenger flows. We use this new model as the environment of the traffic in mass transit for the reinforcement learning optimization. For this aim, we defined, under the new traffic model, the state variables as well as the control ones, including in particular the number of running vehicles, the vehicle dwell times at stations, and the passenger inflow to platforms. Second, we present our new deep Q-network (DQN) model for the reinforcement learning (RL) with the state representation, action space, and reward function definitions. We also provide the neural network architecture as well as the main hyper-parameters. Finally, we give an evaluation of the model under multiple scenarios. We show in particular the efficiency of the control of the passenger inflows into the platforms.

Simulation-based analysis of different curb space type allocations on curb performance

Curbspace is a limited resource in urban areas. Delivery, ridehailing and passenger vehicles must compete for spaces at the curb. Cities are increasingly adjusting curb rules and allocating curb spaces for uses other than short-term paid parking, yet they lack the tools or data needed to make informed decisions. In this research, we analyse and quantify the impacts of different curb use allocations on curb performance through simulation. Three metrics are developed to evaluate the performance of the curb, covering productivity and accessibility of passengers and goods, and CO2 emissions. The metrics are calculated for each scenario across a range of input parameters (traffic volume, parking rate, vehicle dwell time, and street design speed) and compared to a baseline scenario. This work can inform policy decisions by providing municipalities tools to analyse various curb management strategies and choose the ones that produce results more in line with their policy goals.

An empirical analysis of passenger vehicle dwell time and curb management strategies for ride-hailing pick-up/drop-off operations

An empirical analysis of passenger vehicle dwell time and curb management strategies for ride-hailing pick-up/drop-off operations

Do parcel lockers reduce delivery times? Evidence from the field

Common-carrier parcel lockers have emerged as a secure, automated, self-service means of delivery consolidation in congested urban areas, which are believed to mitigate last-mile delivery challenges through reducing out-of-vehicle delivery times and consequently vehicle dwell times at the curb. However, little research exists to empirically demonstrate the environmental and efficiency gains from this technology. In this study, we designed a nonequivalent groups pre-test/post-test control experiment to estimate the causal effects of a parcel locker on delivery times in a residential building in downtown Seattle, WA. The causal effects are measured in terms of vehicle dwell time and the time delivery couriers spend inside the building, through the difference-in-difference method and using a similar nearby residential building as a control. The results showed a statistically significant decrease in time spent inside the building, and small yet insignificant reduction in delivery vehicle dwell time at the curb. The locker was also well received by the building managers and residents.

CCITL: A cloud‐based smart traffic management protocol using intelligent traffic light system in VANETs

SummaryRoad congestion and traffic jams are serious problems for drivers, causing them more delay in reaching their destinations. Motivated by these considerations, this article proposes an intelligent scheduling algorithm based on vehicular ad hoc networks (VANETs) and Cloud computing to schedule traffic lights to smooth road traffic and reduce waiting time on the road, called CCITL (cloud computing based intelligent traffic light protocol). In CCITL, we involve cloud computing by using conventional and vehicular clouds to have a global view of the road network and take advantage of the cloud computing capabilities to calculate the most appropriate traffic signal formulas and combinations with their phase durations in a dynamic way. This allows to ensure the fluidity of the traffic and the arrival at the destination in a short time while minimizing the waiting time on the different segments of the road network. Our proposed protocol has been developed and evaluated over OMNET++ simulator with the simulator of urban mobility (SUMO). The simulation results showed that CCITL greatly contributed to the alleviation of traffic congestion as well as to the reduction of the dwell time of vehicles in the different segments, thus reducing delays and waiting in intersections.

Recognition of Vehicles Entering Expressway Service Areas and Estimation of Dwell Time Using ETC Data.

To scientifically and effectively evaluate the service capacity of expressway service areas (ESAs) and improve the management level of ESAs, we propose a method for the recognition of vehicles entering ESAs (VeESAs) and estimation of vehicle dwell times using electronic toll collection (ETC) data. First, the ETC data and their advantages are described in detail, and then the cleaning rules are designed according to the characteristics of the ETC data. Second, we established feature engineering according to the characteristics of VeESA and proposed the XGBoost-based VeESA recognition (VR-XGBoost) model. Studied the driving rules in depth, we constructed a kinematics-based vehicle dwell time estimation (K-VDTE) model. The field validation in Part A/B of Yangli ESA using real ETC transaction data demonstrates that the effectiveness of our proposal outperforms the current state-of-the-art. Specifically, in Part A and Part B, the recognition accuracies of VR-XGBoost are 95.9% and 97.4%, respectively, the mean absolute errors (MAEs) of dwell time are 52 and 14 s, respectively, and the root mean square errors (RMSEs) are 69 and 22 s, respectively. In addition, the confidence level of controlling the MAE of dwell time within 2 min is more than 97%. This work can effectively recognize the VeESA and accurately estimate the dwell time, which can provide a reference idea and theoretical basis for the service capacity evaluation and layout optimization of the ESA.

Studying the regularities in public transport vehicle dwell time required for passenger boarding and deboarding

The review of the literature devoted to the research of public transport vehicle (PTV) dwell time required for passenger boarding-deboarding at a stop confirms the importance of taking this time into account when modelling passenger transportation. The data about the dwell time were collected in different time periods for different PTVs in various cities and countries. Thus, there is no general model allowing to define the distribution parameters of dwell time variable and answer the question on the regularities in time values. So, the research of the PTV dwell time at the public transport stops in Kharkiv remains actual. Goal. The search of the regularities in transport system performance indicators including PTV dwell time at a stop will allow to apply relevant mathematical methods for the development of the forecast models which are valuable in the field of organization of passenger transportation and servicing. Methodology. The developed methodology to collect data about PTV dwell time at the stops will allow receiving high-quality survey data. The designed survey sheets enable a surveyor to record all needed information and prepare it for processing. The collected data will allow to define the distribution of the PTV dwell time at a stop. Results. The conducted PTV dwell time survey allowed to collect the sufficient amount of data to estimate the distribution of this variable. During the research, it was determined that the empirical dwell time distribution can be well described with the theoretical gamma distribution. The latter distribution appeared to be applicable for all surveyed PTVs. Originality. The defined distribution of the PTV dwell time at a stop for passenger boarding-deboarding allows receiving the results of passenger flows modelling which are more precise compared to the modelling with no dwell time consideration. The use of the dwell time regularities in the procedure of passenger flows assignment results in the increased precision of flow volumes estimation by up to 14.9 % – from 2.28 % to 1.94 %. Practical value. The received results support the fact that the research of PTV dwell time at a stop is actual, and the solution of the task of dwell time distribution estimation will make it possible to improve passenger flows modelling in public transport route systems.

Passenger’s routes planning in stochastic common-lines’ multi-modal transportation network through integrating Genetic Algorithm and Monte Carlo simulation

In the urban transportation network, most passengers choose public transportation to travel. However, bad weather, accidents, traffic jams and other factors lead to uncertainty in transportation network. Besides, transport vehicles running on the same segments of routes and belonging to different modes or routes make the transportation network more complicated. In order to improve the efficiency of passenger’s travel, this paper aim to introducing an approach for optimizing passenger travel routes. This approach takes the travel cost and the number of transfers as constraints to finding the shortest total travel duration of passenger in urban transporta-tion network. The running duration and dwell duration of the vehicles in the network are uncertain, and the vehicles are running according to the timetables. As transportation modes, bus, rail transit and walk are consid-ered. In terms of methodological contribution, this paper combines Genetic Algorithm (GA) and Monte Carlo simulation to deal with optimization problem under stochastic conditions. This paper uses Monte Carlo simula-tion to simulate the running duration and dwell time of vehicles in different scenarios to deal with the uncertainty of the network. The shortest path of passenger’s travel is solved by GA. Two kinds of population management strategies including single population management strategy and multiple population management strategy are designed to guide the solution population evolving process. The two kinds of population management strategies of GA are tested in numerical example. The satisfactory convergence performance and efficiency of the model and algorithm is verified by the numerical example. The numerical example also demonstrated that the multiple population management strategy of GA can get better results in a shorter CPU time. At the same time, the influ-ences of some significant variables on solution are performed. This paper is able to provide a scientific quantita-tive support to the path scheme selection under the influence of common-lines and timetables of different modes of transportation in stochastic urban multimodal transportation network.

Empirical analysis of commercial vehicle dwell times around freight-attracting urban buildings in downtown Seattle

Dwell time is defined as the time that delivery workers spend performing out-of-vehicle activities while their vehicle is parked. Restricting vehicle dwell time is widely used to manage commercial vehicle parking behavior. However, there is insufficient data to help assess the effectiveness of these restrictions. This makes it difficult for policymakers to account for the complexity of commercial vehicle parking behavior. The current study aims to identify factors correlated with dwell time for commercial vehicles. This is accomplished by using generalized linear models with data collected from five buildings that are known to include commercial vehicle activities in the downtown area of Seattle, Washington, USA. Our models showed that dwell times for buildings with concierge services tended to be shorter. Deliveries of documents also tended to have shorter dwell times than oversized supplies deliveries. Passenger vehicle deliveries had shorter dwell times than deliveries made with vehicles with roll-up doors or swing doors (e.g., vans and trucks). When there were deliveries made to multiple locations within a building, the dwell times were significantly longer than dwell times made to one location in a building. The findings from the presented models demonstrate the potential for improving future parking policies for commercial vehicles by considering data collected from different building types, delivered goods, and vehicle types.

Predicting Commercial Vehicle Parking Duration using Generative Adversarial Multiple Imputation Networks

As the world rapidly urbanizes in pace with economic growth, the rising demand for products and services in cities is putting a strain on the existing road infrastructure, leading to traffic congestion and other negative externalities. To mitigate the impacts of freight movement within commercial areas, city planners have begun focusing their attention on the parking behaviors of commercial vehicles. Unfortunately, there is a general lack of information on such activities because of the heterogeneity of practices and the complex nature of urban goods movement. Furthermore, field surveys and observations of truck parking behavior are often faced with significant challenges, resulting in the collection of sparse and incomplete data. The objective of this study is to develop a regression model to predict the parking duration of commercial vehicles at the loading bays of retail malls and identify significant factors that contribute to this dwell time. The dataset used in this study originates from a truck parking and observation survey conducted at the loading bays of nine retail malls in Singapore, containing information about the trucks’ and drivers’ activities. However, because of the presence of incomplete fields found in the dataset, the authors propose the use of a generative adversarial multiple imputation networks algorithm to impute the incomplete fields before developing the regression model using the imputed dataset. Through the parking duration model, the activity type, parking location, and volume of goods delivered (or picked up) were identified as significant features influencing vehicle dwell time, corroborating with findings in the literature.

Investigating the Impact of Dwell Time on the Reliability of Urban Light Rail Operations

The present study investigates the determinants of vehicle dwell time at stations in urban light rail networks. Using data collected from an on-board automatic passenger counting system of the tramway network of the French city of Nantes over a long period, the study performs graphical and statistical analyses enabling the identification of cause-and-effect relationships of a number of attributes on the dwell time and its reliability. The results confirm the significance of the boarding and alighting passenger volumes, as well as of the on-board passenger loading, on the dwell time. Additional effects on dwell time are found from the vehicle type (low- or high-floor), the time of day (peak, off-peak, inter-peak) and the location of the station (city centre, proximity to points of interest). Also, it is found that operations are not symmetrical, and dwell times tend to be higher in one direction than the other of the same line. Finally, the results suggest that dwell time reliability is lower for stations located further from the starting terminal, or for stations located in the city centre.

Performance analysis and fleet requirements of automated demand-responsive transport systems as an urban public transport service

The introduction of public transport services by fully automated vehicles can potentially change the way public transit services will be operated, as they allow shifting from rigid scheduled and route-bound services towards flexible, demand-responsive services. This study examines the potential performance of an Automated Demand Responsive Transport Service (ADRTS) as a replacement for scheduled bus services and simulates the effects of demand levels, vehicle capacity, vehicle dwell time and the initial vehicle distribution on system performance in terms of fleet size and system costs. The simulation tool allows simulating the operation of the ADRTS in a complete graph and is applied to the case study of Arnhem, the Netherlands. For this case study it has been shown that for a minimum fleet size following the imposed constraints, the operational costs range between 0.84 and 1.22 Euros and the average passenger wait time ranges between 2 and 6 minutes, according to the assumptions made on demand and operational parameters. The operational costs of the ADRTS showed to be in the same range of the current bus system, while providing a demand-responsive transport service with an average waiting time of around 4 minutes per passenger-trip. The economies of scale, which play an important role in public transport, are also apparent in the simulated ADRTS operations.

Analysis of the resources provision of stopping points of transport-transfer stations of urban passenger transport

It is proposed to consider the efficiency of the operation of transport-transfer stations in terms of the effect of resource provision of stop points on the duration of the stay of passengers in them. Based on the principles of stabilizing the work of urban public passenger transport and the conditions for sustainable development of the urban environment, the structure of the contour connection of technological solutions is identified. The functional task is ensuring the level of organization aimed at maximizing the service-resource potential of routes and neutralizing the negative consequences of the work of transport and transfer units. The presented connection is based on taking into account the multilevel representation of the results of the work of the elements of transport and transfer stations on the service-resource potential of urban public passenger transport routes and the social sphere of the urban environment. The proposed communication allows to substantiate the general form and structure of the criterion for assessing the efficiency of transport-transfer stations. The presented efficiency criterion takes into account the limitations of the influence of the operation organization of stop stations on the time of the passengers' movement through the TTS, the quality of traffic on the adjacent section of the road network and the urban environment. On the basis of experimental studies, the characteristic dependence of the influence of the resource supply of stop points on the efficiency of their operation has been established. The obtained form of the function of the influence of resource provision of stopping points on the duration of dwell time of vehicles in the queue, the level of blocking of the roadway of the road network and the environment has an exponential dependence. For the selected stopping point it is established that the acceptable level from the point of view of providing service quality is the reserve capacity level in the range of 0.1–0.4. With an increase in the reserve capacity from 0.1 to 0.4: the average total dwell time of vehicles is reduced by 1.6 times (from 3.9 minutes to 2.4 minutes); the average dwell time in the queue is reduced by 5.7 times (from 0.4 minutes to 0.07 minutes); the specific weight blocking the roadway is reduced by 3.6 times (from 0.65 to 0.18). The obtained dependences allow to establish the allowable values of the reserve provision parameters for the stopping points of the transport and transfer stations. Based on the presented methodology for assessing the efficiency of transport-transshipment stations, it is possible to evaluate the feasibility of implementing optimization management measures aimed at streamlining the arrival of vehicles at stop points.

Designing an Automated Demand-Responsive Transport System: Fleet Size and Performance Analysis for a Campus–Train Station Service

The rapid development of automated vehicles and the introduction of shared mobility services hold great potential for the development of automated public transport systems. However, their expected performance under alternative conditions and with alternative design factors remains unknown. In this study, the operation of a new automated mobility service was simulated and evaluated. The proposed transport system was defined as an automated demand-responsive transport system (ADRTS), which consisted of a demand-responsive public transportation service providing sharable rides without fixed routes or timetables in automated vehicles. Requests for the ADRTS were combined if they shared the same pickup and drop-off locations, subject to vehicle capacity limitations. Rides were launched within a predefined maximum vehicle dwell time, and routes were chosen without detours. The proposed system was tested in a field operation pilot project of the operation of automated vehicles in Wageningen in the Netherlands under conditions with different demand patterns, vehicle capacities, and operational factors. The simulation model determined the minimal and optimal fleet size for operation of the shuttle service when total operational and travel costs were minimized and maximum passenger waiting time requirements were satisfied. The results indicated that the most effective way to reduce the system cost per passenger of the ADRTS in the presented case study was to increase the demand level, increase the share of passengers who arrive independently, and use adequate vehicle sizes and short vehicle dwell times.

Airport Curbside and Parking Area Operations at BIA – Analysis of User Behavior

Airport curbside, where travelers and their baggage enter and exit the terminal, and the designated parking area are important components in airport land-side facilities. Passengers expect safe and efficient roadway operations even as volumes increase, but the design and capacity of .the curbside are often constrained by the terminal building and the proximity of on-airport land-side infrastructure. The operating characteristics of airport terminal curbside differ significantly from those of most other roadways due to several reasons such as vehicle dwell time, maneuver vehicles to and from adjacent lane, variation in demand etc. The capacity of a curbside roadway is defined both by the number of vehicles that can be accommodated while stopping to pick up or drop off passengers and the number that can be accommodated while traveling past the curbside in the through lanes. Therefore a study of operations at curbside and parking area is important to identify issues related to existing and future demand levels. The main focus of the paper is on the evaluation of vehicle operations and passenger behavior at the BIA terminal access roadway, weaving segment, arrival and departure curbside roadways and terminal car park. Analysis of vehicular traffic, travel mode choices, and curbside roadway vehicle queues, vehicle dwelling times, passenger occupancy time at curbs and passenger processing and walking times will provide useful information for developing plans for operational improvements as well as for future expansions. Using the available data, the demand and the capacity at these facilities are evaluated to estimate the existing level of service. In addition, measures were identified to improve the operational efficiency of these facilities and design improvements are proposed to ensure good operational efficiency for the forecast future demand. ENGINEER, Vol. 47, No. 04, pp. 43-51, 2014

Boarding and Alighting Experiments

Vehicle dwell time is an important determinant of the system performance and passenger service quality of public transport systems. Adequate prediction models for vehicle dwell time are required in timetable planning and in pedestrian simulation tools to optimize station design and rolling stock design. Because most of the dwell time is consumed by boarding and alighting, this paper focuses on increasing insight into boarding and alighting behavior in railroad vehicles by performing laboratory experiments using video cameras. The experiments test the effects of the physical environment (stepping gap, height difference, and door width), population (age, gender), flow composition (direction, time pressure, luggage, boarding group size, boarding ratio, queuing discipline), and prevailing traffic conditions (crowding on board and crowding on the platform). From the video images, pedestrian trajectories (movements over time) have been derived. This paper describes the effects of the size of the horizontal and vertical gap and the presence of luggage on the door capacity. Door capacities have been estimated by using headway distributions and cumulative curves. Increasing the horizontal and vertical gap leads to decreases in the capacity (up to 15%). When passengers with luggage (suitcases) are present, the capacity decreases more (up to 25%).

Auto-Engaging Mobility-Aid Securement Systems and Low-Floor Buses

The use of auto-engaging mobility-aid securement systems in regular transit operation on high- and low-floor transit vehicles in two cities was investigated in a demonstration project. The two cities were Eugene, Oregon, and Anchorage, Alaska. The impact of the new securement technology on vehicle operators, mobility-aid users, regular passengers, maintenance, and other transit staff was also investigated. Auto-engaging securement provides fast, safe, and independent mobility-aid securement for wheeled mobility aids. The auto-engaging securement systems were retrofitted on a select number of high-floor buses in regular transit service in Eugene, Oregon, and were factory installed on a fleet of new low-floor transit vehicles that were delivered to Anchorage, Alaska, for regular transit operations. Evaluation consisted of written and oral surveys administered to vehicle operators, mobility-aid users, regular passengers, and maintenance and other transit staff before, during, and after the demonstration project. The operational impacts of retrofitted and factory-installed securement systems were also compared. Auto-engaging securement systems show significant promise for use on highand low-floor transit vehicles. Auto-engaging systems do not delay the transit vehicle dwell time. In all tests the mobility-aid users were delighted with the independence and freedom of travel and felt much more secure in the auto-engaging securement systems than in the traditional belt securement systems.

Passenger requirements in an automated transport system : Richardson, J., and Stroud, P.G.Transport and Road Research Laboratory, Department of the Environment, 1975, 151 pp

This report presents the results from an experimental study which provided human factors data on passenger behaviour in relation to certain aspects of vehicle design and station layout on an urban rapid transit system. An extensive series of tests was carried out using full scale mock-up vehicle and platforms to study, inter alia, the effects on vehicle dwell time of: 1 vehicle size; 2 vehicle door size and using separate doors for entry and exit; 3 the number of people in the vehicle, on the platform and inter-changing between the two; 4 overloading the vehicle with passengers; 5 platform size and layout, including the provision of barriers. The tests were mainly carried out using a static vehicle but in some cases the vehicle was moved in and out of the station to determine the effect of vehicle movement, particularly deceleration, on passenger behaviour at the station. The rapid transit system under study will serve as great a proportion as possible of the urban community; the handicapped persons and those inconvenienced with luggage or pushchairs have been given special considerations in the experimental trials. The report also discusses the implication for passenger behaviour, and hence system performance, of choosing certain vehicle and station design features. (A) /TRRL/