Dynamic Assignment Model Research Articles

Transitioning towards the deployment of line-based autonomous buses: Consequences for service frequency and vehicle capacity

Abstract The deployment of autonomous buses (AB) is expected to have consequences for service design facilitated by its cost function structure. We study the impacts of AB deployment in line-based public transport (PT) systems. In particular, we examine the transition phase where AB is sequentially deployed, involving the selection of lines for which AB will be introduced. To this end, we develop a modeling framework using a dynamic public transportation assignment and operations simulation model that captures users’ adaptive path choices. An analytical model is used to determine the initial solutions in terms of service frequency and vehicle capacity for the simulation framework. Due to their different cost function structures, the deployment of AB may be accompanied by changes in the service frequency and vehicle capacity settings and consequently also on passenger flow distribution across the network. Both the simultaneous and the sequential deployment of AB on multiple lines are investigated. Deployment solutions are assessed in terms of the both total operator and user cost. The decision variables are vehicle capacity per line, service frequency per line and vehicle technology per line - i.e. either manually driven or fully automated buses. The framework is applied to a case study in Kista, Stockholm. The study shows that AB service have the potential to attract passengers through improved service provision. A sensitivity analysis is carried out concerning the effects of different cost parameters and demand levels on the deployment of AB in fixed line operations.

Dynamic assignment model of trains and users on a congested urban-rail line

For the management and planning of urban rail lines, operators can draw upon tools that use train circulation models as well as passenger assignment models. However, these two kinds of simulation models are independent. They do not interact as they do in reality. Yet on certain highly congested lines, high train frequency and large passenger volumes can turn a small incident into a delay on the entire line. Our research presents an integrated model for the simulation of a fixed block urban rail line in interaction with passenger assignment. This operational model introduces new management strategies or rolling stock feature solutions to improve the quality of service on the line. A discrete-event approach simulates the progress of the runs on the line, and the representation of the passengers by origin-destination flow per time step makes it possible to effectively simulate lines with large flows. An application to Line 13 of the Paris metro illustrates the model on a real case of congestion. Sensitivity analyses on the level of demand as well as on service characteristics demonstrate the utility of this integrated approach.

SOCIAL INSURANCE AND OCCUPATIONAL MOBILITY

AbstractThis article studies how insurance from progressive taxation improves the matching of workers to occupations. We propose an equilibrium dynamic assignment model to illustrate how social insurance encourages mobility. Workers experiment to find their best occupational fit in a process filled with uncertainty. Risk aversion and limited earnings insurance induce workers to remain in unfitting occupations. We estimate the model using microdata from the United States and Germany. Higher earnings uncertainty explains the U.S. higher mobility rate. When workers in the United States enjoy Germany's higher progressivity, mobility rises. Output and welfare gains are large.

Learning and Adaptation in Dynamic Transit Assignment Models for Congested Networks

The distribution of passenger demand over the transit network is forecasted using transit assignment models which conventionally assume that passenger loads satisfy network equilibrium conditions. The approach taken in this study is to model transit path choice as a within-day dynamic process influenced by network state variation and real-time information. The iterative network loading process leading to steady-state conditions is performed by means of day-to-day learning implemented in an agent-based simulation model. We explicitly account for adaptation and learning in relation to service uncertainty, on-board crowding and information provision in the context of congested transit networks. This study thus combines the underlying assignment principles that govern transit assignment models and the disaggregate demand modeling enabled by agent-based simulation modeling. The model is applied to a toy network for illustration purposes, followed by a demonstration for the rapid transit network of Stockholm, Sweden. A full-scale application of the proposed model shows the day-to-day travel time and crowding development for different levels of network saturation and when deploying different levels of information availability.

Dynamic Demand Forecast and Assignment Model for Bike-and-Ride System

Bike-and-Ride (B&R) has long been considered as an effective way to deal with urbanization-related issues such as traffic congestion, emissions, equality, etc. Although there are some studies focused on the B&R demand forecast, the influencing factors from previous studies have been excluded from those forecasting methods. To fill this gap, this paper proposes a new B&R demand forecast model considering the influencing factors as dynamic rather than fixed ones to reach higher forecasting accuracy. This model is tested in a theoretical network to validate the feasibility and effectiveness and the results show that the generalised cost does have an effect on the demand for the B&R system.

From Traditional to Automated Mobility on Demand: A Comprehensive Framework for Modeling On-Demand Services in SimMobility

Mobility on demand (MoD) systems have recently emerged as a promising paradigm for sustainable personal urban mobility in cities. In the context of multi-agent simulation technology, the state-of-the-art lacks a platform that captures the dynamics between decentralized driver decision-making and the centralized coordinated decision-making. This work aims to fill this gap by introducing a comprehensive framework that models various facets of MoD, namely heterogeneous MoD driver decision-making and coordinated fleet management within SimMobility, an agent- and activity-based demand model integrated with a dynamic multi-modal network assignment model. To facilitate such a study, we propose an event-based modeling framework. Behavioral models were estimated to characterize the decision-making of drivers using a GPS dataset from a major MoD fleet operator in Singapore. The proposed framework was designed to accommodate behaviors of multiple on-demand services such as traditional MoD, Lyft-like services, and automated MoD (AMoD) services which interact with traffic simulators and a multi-modal transportation network. We demonstrate the benefits of the proposed framework through a large-scale case study in Singapore comparing the fully decentralized traditional MoD with the future AMoD services in a realistic simulation setting. We found that AMoD results in a more efficient service even with increased demand. Parking strategies and fleet sizes will also have an effect on user satisfaction and network performance.

Dynamic Passenger Assignment for Major Railway Disruptions Considering Information Interventions

Passenger assignment models for major disruptions that require trains to be cancelled/short-turned in railway systems are rarely considered in literature, although these models could make a significant contribution to passenger-oriented disruption timetable design/rescheduling. This paper proposes a dynamic passenger assignment model, where the passengers who start travelling before, during and after the disruption are all considered. The model ensures that on-board passengers are given priority over waiting passengers, and waiting passengers are boarding under the first-come-first-serve rule. Moreover, the model allows information interventions by publishing information about service variations and train congestion at different locations with the aim of distributing passengers wisely to achieve less travel time increase due to the disruption. Discrete event simulation is adopted to implement the model, where loading/unloading procedures are realized and passengers re-plan their paths based on the information they receive. The model tracks individual travels, which helps to evaluate a disruption timetable in a passenger-oriented way.

Frequency and Vehicle Capacity Determination using a Dynamic Transit Assignment Model

We integrate for the first time, to our knowledge, a dynamic transit assignment model into the tactical planning phase. The settings of service frequencies and vehicle capacities determine line capacity and have significant consequences for level-of-service and operational costs. The objective of this study is to determine frequency and vehicle capacity at the network level while accounting for the impact of service variations on users and operator costs. To this end, we propose a simulation-based optimization approach. The proposed model allows accounting for variations in service headways and crowding as well as their consequences for passenger flows distribution, all of which have not been accounted for in the tactical planning so far. Practical benefits of the model are demonstrated by an application to a bus network in the Amsterdam metropolitan area. This study contributes to the development of a new generation of methods that integrate reliability into the tactical planning phase to improve service quality.

Research on Dynamic Rotational Grazing Assignment Model based on Grass Resource Leveling

Rotational grazing is an effective way to improve grassland resources utilization and develop animal husbandry. Rational grazing scheme can avoid eco-environment damage caused by overgrazing, and it can ensure the livestock normal feeding. The rationality of rotational grazing management is affected by many complex and dynamic factors. This paper establishes a model of rotational grazing assignment based on grass resource leveling. Firstly, each rotational grazing plot’s forage yield in each rotational grazing assignment stage is predicted by seasonal prediction method. Then, constrained by the rotational grazing plots’ forage yield and livestock’s forage demand, a dynamic rotational grazing assignment model is established, which combines the assignment model and carrying capacity constraint. Thus, the maximum edible forage in a rotational grazing cycle is contained and the distribution of the corresponding rotational grazing plots is determined. In each rotational grazing cycle, each rotational grazing plot can only be assigned once, which embodies the principle of ecological protection. And livestock have as much forage as possible in a rotational grazing cycle. Finally, through a case analysis, a feasible solution in a rotational grazing cycle is obtained. Through taking carrying capacity in each rotational grazing assignment stage as a standard to scheme the grazing order in each plot, the uncertainty of eco-environment is better reflected. A reasonable decision scheme of dynamic rotational grazing can make resources used efficiently in time and space.

Dynamic management of loading bays for energy efficient urban freight deliveries

A model for dynamic assignment of loading bays for urban last-mile deliveries has been developed. It aims to solve the problem of defining the most optimal number and location of loading bays and their management for energy efficient urban freight deliveries. Optimisation is based on fuzzy k-means clustering of receivers to dynamically select the best possible loading bay in combination with a routing algorithm. The model is tested on the actual data of deliveries in the historical city centre of Lucca, Italy. The results of simulations have demonstrated a significant savings of time and distance travelled by freight vehicles, as well as of CO2 and fuel, in comparison to the existing situation.

Potential-based dynamic pedestrian flow assignment

This study proposes a potential-based dynamic pedestrian flow assignment model to optimize the evacuation time needed for all pedestrians to leave an indoor or outdoor area with internal obstacles and multiple exits, e.g., railway station, air terminal, plaza, and park. In the model, the dynamic loading of pedestrian flows on a two-dimensional space is formulated by a cell transmission model, the movement of crowds is driven by space potential, and the optimization of evacuation time is solved by a proportional swapping process. In this way, the proposed model can be applied to not only efficiently optimize the evacuation process of a crowd with large scale but also recognize local congestion dynamics during crowd evacuation. Finally, a set of numerical examples are presented to show the proposed model’s effectiveness for optimizing crowd evacuation process and its application to design a class of variable guide sign systems.

A modeling of dynamic storage assignment for order picking in beverage warehousing with Drive-in Rack system

This paper develops a dynamic storage assignment model to solve storage assignment problem (SAP) for beverages order picking in a drive-in rack warehousing system to determine the appropriate storage location and space for each beverage products dynamically so that the performance of the system can be improved. This study constructs a graph model to represent drive-in rack storage position then combine association rules mining, class-based storage policies and an arrangement rule algorithm to determine an appropriate storage location and arrangement of the product according to dynamic orders from customers. The performance of the proposed model is measured as rule adjacency accuracy, travel distance (for picking process) and probability a product become expiry using Last Come First Serve (LCFS) queue approach. Finally, the proposed model is implemented through computer simulation and compare the performance for different storage assignment methods as well. The result indicates that the proposed model outperforms other storage assignment methods.

Bi-Level Programming Model for Dynamic Reversible Lane Assignment

To improve the space utilization and traffic capacity of a signalized junction, this paper proposes a dynamic reversible lane assignment method for approaches of signalized junctions that consider the game equilibrium between road users and traffic controllers. To theoretically analyze the behaviors of the players involved in the leader-follower strategic game, a bi-level programming model is established. To minimize the total queue length of approaches at signalized junctions, the upper model dynamically optimizes the reversible lane assignments and can be solved with the enumeration method or the Monte Carlo algorithm. The lower model outputs the traffic assignment at road sections using a Logit-based stochastic user equilibrium model that is solved by the method of successive averages. The general impedance of road section in lower model consists of the road travel time cost and the intra-time cost at the signalized junction connected with current road section. In addition, the interaction between the two level is simulated in an iterative optimization procedure. Finally, this paper uses two numerical experiments to validate the proposed approach.

An expert knowledge-based dynamic maintenance task assignment model using discrete stress–strength interference theory

Expert knowledge has become an important factor in optimization decision-making for complex equipment maintenance. Motivated by the challenges of quantifying expert knowledge as a decision basis, we presented an expert knowledge-based dynamic maintenance task assignment model by using discrete stress–strength interference (DSSI) theory. We constructed the task assignment framework consisting of three parts: building expert database, selecting experts for tasks, and implementing the tasks, in which selecting experts for tasks based on expert knowledge is the key part of the model. To quantify tacit knowledge (experience) in optimization decision for expert recommendation, experience was defined as a probability, which is relevant to two random variables: quantity of task successfully implemented (strength) and quantity of task failed (stress), and experience is defined as the probability that the former (strength) is larger than the latter (stress). Further, universal generating function (UGF) method was used to calculate the experience, and decision rule was designed for the dynamic maintenance task assignment. The model can help collaborative maintenance platform periodically review experts’ performances and assign the corresponding task to the most suitable expert at different periods. A case study shows that the proposed model helps not only to achieve rational allocation of expert resources, but to promote positive competition among experts.

Analysis of aircraft ground traffic flow and gate utilisation using a hybrid dynamic gate and taxiway assignment algorithm

ABSTRACTThe rapid increase in the demand for air transportation over the last four decades has led to serious capacity problems for both the airside and landside components of major airports. The efficient management of existing airside resources seems to be the most effective and practical approach to overcome these capacity and traffic flow problems. Although integrated management of aircraft parking position assignments and ground movement planning processes are vital for the effective use of resources and for efficient operations, the current practice is that these processes are handled separately by different agents. This study proposes a hybrid dynamic system, an integrated methodology of taxi path and gate assignment using a knowledge-based decision-making approach to model effectively time-variant and realistic operational features of aircraft gate management and route planning. The model assigns the most suitable parking positions with minimum taxi time and taxi delay among a reduced solution set, satisfying pre-defined decision criteria as well as monitoring ground movements and, if necessary, reassigning new taxi paths and parking positions in real time. Both the proposed integrated methodology and the separate gate assignment and ground management operations currently in use were implemented, analysed and compared in a fast-time simulation model of Istanbul Ataturk Airport (LTBA). The hybrid dynamic assignment model provided significant improvements in taxi times, ground delays and gate utilisation.

Analysis of road urban transport network capacity through a dynamic assignment model: validation of different measurement methods

Abstract Network capacity in a transportation system becomes an important measurement for transport planning and management because it addresses its capability to satisfy an efficient network traffic flow reducing the inefficiency of congestion phenomena. This work provides a discussion of road urban transport network capacity including existing definitions in literature and the validation of new measurement methods. The study explores some of the properties of network-wide traffic flow relationships in a large-scale complex urban street network using real-time simulated results obtained from a dynamic traffic assignment model, periodically updated by data from radar sensors through rolling horizon technics. The basic variables used in the methodology, such as network flows and speeds, are characterized using a network model calibrated in the urban area of Catania (Italy). For a comprehensive yet simple analysis, equations and graphs are utilized to resume the obtained results related to different days and several time intervals of the day. This procedure proved to be suitable to investigate the properties of network-level traffic flow relationships and concluding remarks include suggestions for further research in this highly promising area.

Simulation-Based Dynamic Passenger Flow Assignment Modelling for a Schedule-Based Transit Network

The online operation management and the offline policy evaluation in complex transit networks require an effective dynamic traffic assignment (DTA) method that can capture the temporal-spatial nature of traffic flows. The objective of this work is to propose a simulation-based dynamic passenger assignment framework and models for such applications in the context of schedule-based rail transit systems. In the simulation framework, travellers are regarded as individual agents who are able to obtain complete information on the current traffic conditions. A combined route selection model integrated with pretrip route selection and entrip route switch is established for achieving the dynamic network flow equilibrium status. The train agent is operated strictly with the timetable and its capacity limitation is considered. A continuous time-driven simulator based on the proposed framework and models is developed, whose performance is illustrated through a large-scale network of Beijing subway. The results indicate that more than 0.8 million individual passengers and thousands of trains can be simulated simultaneously at a speed ten times faster than real time. This study provides an efficient approach to analyze the dynamic demand-supply relationship for large schedule-based transit networks.

An Enhanced Dynamic User Optimal Passenger Flow Assignment Model for Metro Networks

By considering the difference between a car driver’s route choice behavior on the road and a passenger’s route choice behavior in urban rail transit (URT), this paper proposes an enhanced Dynamic User Optimal (DUO) passenger flow assignment model for metro networks. To capture realistic URT phenomena, the model has integrated the train operation disturbance constraint. Real passenger and train data are used to verify the proposed model and algorithm. The results indicate that the DUO-based model is more suitable for describing passenger route choice behavior under uncertain conditions compared to a static model. Moreover, this paper found that passengers under oversaturated conditions are more sensitive to train operation disturbances compared to undersaturated passengers.

Formulation of the Transit Link Transmission Model

Abstract In this paper we present a new kind of dynamic assignment model for public transport, including transit and pedestrian networks, that is capable of representing single runs, whose schedule is possibly affected by congestion. To this end we avoid introducing explicitly a diachronic graph and rely just on a spatial/functional graph, like in frequency based models. More specifically, we here extend to transit networks the framework of the Link Transmission Models, so far applied only to road networks (Yperman, 2007; Gentile, 2010; Gentile, 2015). The focus of LTM is the propagation, affected by congestion, of flows on the network, for given route choices. In this framework, both schedule based and frequency based passenger behaviors can be implicitly simulated by properly setting time-varying splitting rates. The core of the proposed Transit Link Transmission Model (TLTM) is the node model, which aims at reproducing the flow conflicts (congestion) occurring at bus stops and rail platforms, such as: passengers that fail-to-board or fail-to-sit due to vehicle overcrowding, vehicles queueing to serve a stop, doors opened longer to allow passengers alighting and boarding. The formulation and implementation of the TLTM will be presented in the following, while the application of the model on test and real networks will be presented in a forthcoming paper.

Beyond a complete failure: the impact of partial capacity degradation on public transport network vulnerability

Disruptions in public transport networks (PTNs) often lead to partial capacity reductions rather than complete closures. This study aims to move beyond the vulnerability analysis of complete failures by investigating the impacts of a range of capacity reductions on PTN performance. The relation between network performance and the degradation of line or link capacities is investigated by establishing a vulnerability curve and related metrics. The analysis framework is applied to a full-scan analysis of planned temporary line-level capacity reductions and an analysis of unplanned link-level capacity reductions on the most central segments in the multi-modal rapid PTN of Stockholm, Sweden. The impacts of capacity reductions are assessed using a non-equilibrium dynamic public transport operations and assignment model. The nonlinear properties of on-board crowding, denied boarding, network effects and route choice result in non-trivial, generally convex, relations which carry implications on disruption planning and real-time management.