Dynamic demand estimation and microscopic traffic simulation of a university campus transportation network

Abstract

This article documents the authors' experience with the modeling, simulation, and analysis of a university transportation system, using the TRansportation ANalysis and SIMulation System (TRANSIMS). The processes of data preparation and network coding are described, followed by the algorithm developed to estimate the dynamic 24-hour demand, which includes a procedure for estimating the ‘desirability’ of the different parking lots from readily available data. The dynamic demand estimation algorithm is validated by comparing estimated and observed parking lot occupancies, where it is shown that the algorithm is capable of replicating observed results. Finally, an example is included to demonstrate how the developed model can be used in campus transportation planning. Besides serving as a first case study for using TRANSIMS to model a university campus, the study's contributions include the development of a procedure for parking lot desirability ranking and a practical procedure for estimating dynamic demand on university campuses.     

Developing an enhanced weight-based topological map-matching algorithm for intelligent transport systems

Map-matching (MM) algorithms integrate positioning data from a Global Positioning System (or a number of other positioning sensors) with a spatial road map with the aim of identifying the road segment on which a user (or a vehicle) is travelling and the location on that segment. Amongst the family of MM algorithms consisting of geometric, topological, probabilistic and advanced, topological MM (tMM) algorithms are relatively simple, easy and quick, enabling them to be implemented in real-time. Therefore, a tMM algorithm is used in many navigation devices manufactured by industry. However, existing tMM algorithms have a number of limitations which affect their performance relative to advanced MM algorithms. This paper demonstrates that it is possible by addressing these issues to significantly improve the performance of a tMM algorithm. This paper describes the development of an enhanced weight-based tMM algorithm in which the weights are determined from real-world field data using an optimisation technique. Two new weights for turn-restriction at junctions and link connectivity are introduced to improve the performance of matching, especially at junctions. A new procedure is developed for the initial map-matching process. Two consistency checks are introduced to minimise mismatches. The enhanced map-matching algorithm was tested using field data from dense urban areas and suburban areas. The algorithm identified 96.8% and 95.93% of the links correctly for positioning data collected in urban areas of central London and Washington, DC, respectively. In case of suburban area, in the west of London, the algorithm succeeded with 96.71% correct link identification with a horizontal accuracy of 9.81 m (2σ). This is superior to most existing topological MM algorithms and has the potential to support the navigation modules of many Intelligent Transport System (ITS) services.     

A G/G(n)/C/C state‐dependent simulation model for metro station corridor width design

Metro station corridor and passengers are described as a G/G(n)/C/C state‐dependent queuing system with a general random arrival interval G and a general random and state‐dependent service time G(n) to offset the shortcomings in existing design methods. The corresponding G/G(n)/C/C state‐dependent discrete event simulation model is developed, and its high‐fidelity is tested. Then the optimization algorithm based on the simulation model is designed to determine corridor width. The proposed simulation optimization method and the existing analytical optimization methods, based on M/G(n)/C/C and D/D/1/C queuing models, are applied to design corridor width in a numerical example of 48 combinations of passenger flow rates and level of service (LOS). The designed corridor widths are tested in a micro‐simulation model, and the performance measure is compared. The result shows that the corridor widths obtained by the new method are 0.357 m (7.4%) larger than that of the other two methods on average; the area per passenger of the new method increases 10.53% and 11.63%, respectively, compared with that of the other two methods; the widths designed by the new method satisfy the requirement of LOS under various passenger flows, whereas 93% of the corridor widths obtained by the other two methods fail to meet the requirement of LOS, and the corridor widths designed by the new method have high elasticity coefficients of LOS‐width. Copyright © 2015 John Wiley & Sons, Ltd.     

Optimal allocation of limited and random network resources to discrete stochastic demands for standardized cargo transportation networks

We consider the resource allocation problem with discrete random demands and discrete random resource capacities for standardized cargo transportation networks, in which a freight operator needs to determine the integral quantity of booking requests to be accepted for each product to maximize the expected profit. We formulate the problem as a stochastic integer programming model and provide theoretical results that completely characterize the optimal solution to the stochastic model under a special case. We present a progressive augmentation algorithm and a sampling based method for solving the stochastic model under a general case. We also offer numerical experiments to test the two methods and shed light on their performances.     

Shipment composition enhancement at a consolidation center

When items of different shapes, sizes and weights are transported, some item combinations make most effective use of a vehicle's capacity. A consolidation center, receiving shipments of various items from different origins, can act as a point where those combinations can be formed. While sending shipments through the center invariably increases the total item-miles traveled, judicious shipping can reduce the vehicle-miles traveled. This paper examines ways in which loads should be made up to achieve as large a reduction in vehicle-miles as possible. The paper first considers a building block in which items are sent directly from one origin to one destination, and then analyzes a terminal serving many origins and one destination. The understanding developed from the building block leads to a linear programming formulation of the load make-up/routing problem that arises with many origins. The paper also presents a decomposition principle, and a matching algorithm that can be used to solve the problem in the important special case when vehicles can carry many items. The algorithm has a simple physical interpretation and does not require much data. It can be implemented by hand, or on an inexpensive computer.     

Man and his transport behaviour Part 4. Fare strategies for public transport—a problem outline

Abstract

In April 1983, the “Fourth World Conference on Transport Research” was held in Hamburg. For a period of four days, experts from all parts of the world discussed transport research and planning problems. The discussions were divided into eight so‐called ‘sub‐topics’. Fortunately, one of the sub‐topics, ‘Man and his Transport Behaviour’ (chaired by Moshe Ben‐Akiva, U.S.A. and Werner Brög, Federal Republic of Germany) dealt with the individual and his behaviour. This complemented the traditionally supply‐oriented thinking of the transport planners by introducing the demand component which had frequently been neglected in the past. Since the view has become increasingly widespread that transport is meant to serve people, and thus, that research should emphasize the (potential) users of the transport system, the number of papers submitted and presented on this sub‐topic was especially large.

However, the number of papers which could be included in the ‘Conference Proceedings’ was limited and this would have meant that a number of interesting documents could not be published. Therefore, papers on four special areas within this sub‐topic are to be published in four consecutive issues of Transport Reviews. The areas which will be dealt with are ‘Telecommunications’, ‘Non‐Motorised Transport’, ‘Special Problems in Third World Countries’ and ‘Fare Structures in Public Transport’. The papers were selected strictly according to their contents. A brief commentary in each issue explains in turn the reasons for choosing each of the four subject areas.

Parts 1–3 appeared in Transport Reviews, 4, 99–113, 173–212, 273–298.     

Forecasting short-term subway passenger flow under special events scenarios using multiscale radial basis function networks

Reliable and accurate short-term subway passenger flow prediction is important for passengers, transit operators, and public agencies. Traditional studies focus on regular demand forecasting and have inherent disadvantages in predicting passenger flows under special events scenarios. These special events may have a disruptive impact on public transportation systems, and should thus be given more attention for proactive management and timely information dissemination. This study proposes a novel multiscale radial basis function (MSRBF) network for forecasting the irregular fluctuation of subway passenger flows. This model is simplified using a matching pursuit orthogonal least squares algorithm through the selection of significant model terms to produce a parsimonious MSRBF model. Combined with transit smart card data, this approach not only exhibits superior predictive performance over prevailing computational intelligence methods for non-regular demand forecasting at least 30 min prior, but also leverages network knowledge to enhance prediction capability and pinpoint vulnerable subway stations for crowd control measures. Three empirical studies with special events in Beijing demonstrate that the proposed algorithm can effectively predict the emergence of passenger flow bursts.     

The underground economy: Tracking the higher-order economic impacts of the São Paulo Subway System

Over one million workers commute daily to São Paulo City center, using different modes of transportation. The São Paulo subway network reaches 74.2 km of length and is involved in around 20% of the commuting trips by public transportation, enhancing mobility and productivity of workers. This paper uses an integrated framework to assess the higher-order economic impacts of the existing underground metro infrastructure. We consider links between mobility, accessibility and labor productivity in the context of a detailed metropolitan system embedded in the national economy. Simulation results from a spatial computable general equilibrium model integrated to a transportation model suggest positive economic impacts that go beyond the city limits. While 32% of the impacts accrue to the city of São Paulo, the remaining 68% benefit other municipalities in the metropolitan area (11%), in the State of São Paulo (12.0%) and in the rest of the country (45%).     

Useful estimation procedures for critical gaps

Many different methods for the estimation of critical gaps at unsignalized intersections have been published in the international literature. This paper gives an overview of some of the more important methods. These methods are described by their characteristic properties. For comparison purposes a set of quality criteria has been formulated by which the usefulness of the different methods can be assessed. Among these one aspect seems to be of primary importance. This is the objective that the results of the estimation process should not depend on the traffic volume on the major street during the time of observation. Only if this condition is fulfilled, can the estimation be applied under all undersaturated traffic conditions at unsignalized intersections. To test the qualification of some of the estimation methods under this criterion, a series of comprehensive simulations has been performed. As a result, the maximum likelihood procedure (as it has been described by Troutbeck) and the method developed by Hewitt can be recommended for practical application. ©     

A case study of Beijing bus crew scheduling: a variable neighborhood‐based approach

Crew scheduling for bus drivers in large bus agencies is known to be a time‐consuming and cumbersome problem in transit operations planning. This paper investigates a new meta‐heuristics approach for solving real‐world bus‐driver scheduling problems. The drivers' work is represented as a series of successive pieces of work with time windows, and a variable neighborhood search (VNS) algorithm is employed to solve the problem of driver scheduling. Examination of the modeling procedure developed is performed by a case study of two depots of the Beijing Public Transport Group, one of the largest transit companies in the world. The results show that a VNS‐based algorithm can reduce total driver costs by up to 18.1%, implying that the VNS algorithm may be regarded as a good optimization technique to solve the bus‐driver scheduling problem. Copyright © 2015 John Wiley & Sons, Ltd.     

Consistent link flow estimation from counts

Partly because of counting errors and partly because counts may be carried out on different days, traffic counts on links of a network are unlikely to satisfy the flow conservation constraint “flow IN = flow out” at every node of the network. Van Zuylen and Willumsen (1980) have described a method of eliminating inconsistencies in traffic counts when a single count is available for each link in the network. In this paper, the method is extended to the case when more than one count is available on some links of the network. In addition, an algorithm is described for application of the method.     

An evolutionary local search for the capacitated vehicle routing problem minimizing fuel consumption under three-dimensional loading constraints

This study introduces a new practical variant of the combined routing and loading problem called the capacitated vehicle routing problem minimizing fuel consumption under three-dimensional loading constraints (3L-FCVRP). It presents a meta-heuristic algorithm for solving the problem. The aim is to design routes for a fleet of homogeneous vehicles that will serve all customers, whose demands are formed by a set of three-dimensional, rectangular, weighted items. Unlike the well-studied capacitated vehicle routing problem with 3D loading constraints (3L-CVRP), the objective of the 3L-FCVRP is to minimize total fuel consumption rather than travel distance. The fuel consumption rate is assumed to be proportionate to the total weight of the vehicle. A route is feasible only if a feasible loading plan to load the demanded items into the vehicle exists and the loading plan must satisfy a set of practical constraints.To solve this problem, the evolutionary local search (ELS) framework incorporating the recombination method is used to explore the solution space, and a new heuristic based on open space is used to examine the feasibility of the solutions. In addition, two special data structures, Trie and Fibonacci heap, are adopted to speed up the procedure. To verify the effectiveness of our approach, we first test the ELS on the 3L-CVRP, which can be seen as a special case of the 3L-FCVRP. The results demonstrate that on average ELS outperforms all of the existing approaches and improves the best-known solutions for most instances. Then, we generate data for 3L-FCVRP and report the detailed results of the ELS for future comparisons.     

A multicriteria decision support methodology for evaluating airport expansion plans

Rational decision-making requires an assessment of advantages and disadvantages of choice possibilities, including non-market effects (such as externalities). This also applies to strategic decision-making in the transport sector (including aviation). In the past decades various decision support and evaluation methods have been developed in which a market evaluation played a prominent role. The intrinsic limitations of these approaches were also increasingly recognised. Gradually, a variety of adjusted multidimensional methods has been developed over the past years to complement conventional cost–benefit analysis (CBA). These methods aim to investigate and evaluate all relevant impacts of a choice possibility (e.g., project, plan, or programme) on the basis of a multitude of important policy criteria (so-called multicriteria methods). They have a particular relevance in case of non-priced or qualitative effects. There is a clear need for a systematic and polyvalent multicriteria approach to many actual planning issues, such as land use or transportation. This paper offers a new evaluation framework based on a blend of three types of approaches, viz. Regime Analysis, the Saaty method and the Flag Model. All these methods have been developed separately in the past; the paper makes an effort to offer a cohesive framework, which can be used for the e valuation of spatial-economic and environmental-economic policy issues. This new tool is tested by means of a case study on conflicting plans (and policy views) for airport expansion options in the Maastricht area in the southern part of The Netherlands.     

Methods for creating bus timetables

This paper provides alternative methods for constructing bus timetables using passenger load data. It attempts to fulfill six major objectives: to evaluate alternative timetables in terms of required resources; to improve the correspondence of bus departure times with passenger demand; to provide alternative timetables for the schedulers' use in specific scheduling situations; to permit direct bus frequency changes for possible exceptions (known to the schedulers) which do not rely on passenger demand data; to allow the construction of timetables with headway smoothing techniques (similar to that performed manually); and to integrate different headway setting and different timetable construction methods. The procedures developed set the bus departure times for the case of evenly spaced headways and for the case of allowing the headways to be unevenly spaced. In the first case, smoothing techniques are developed in the transition segments between adjacent time periods. In the second case, the departure times are shifted so as to obtain uniform average loads instead of even headways. The final product of the research consists of a set of computer programs which are tested on a heavy bus line in Los Angeles.     

Prioritizing road extension projects with interdependent benefits under time constraint

Since transportation projects are costly and resources are limited, prioritizing or sequencing the projects is imperative. This study was inspired by a client who asked: “I have tens of approved road extension projects, but my financial resources are limited. I cannot construct all the projects simultaneously, so can you help me prioritize my projects?” To address this question, the benefits and costs of all the possible scenarios must be known. However, the impacts (or benefit) of road extension projects are highly interdependent, and in sizable cases cannot be specified thoroughly. We demonstrate that the problem is analogous to the Traveling Salesman Problem (TSP). Dynamic change in travel demand during construction is another aspect of the complexity of the problem. The literature is yet to provide efficient methods for large cases. To this end, we developed a heuristic methodology in which the variation of travel demand during the construction period is considered. We introduce a geometrical objective function for which a solution-finding policy based on “gradient maximization” is developed. To address the projects’ interdependency, a special neural network (NN) model was devised. We developed a search engine hybridized of Ant Colony and Genetic Algorithm to seek a solution to the TSP-like problem on the NN based on gradient maximization. The algorithm was calibrated and applied to real data from the city of Winnipeg, Canada, as well as two cases based on Sioux-Falls. The results were reliable and identification of the optimum solution was achievable within acceptable computational time.     

Designing an integrated distribution system for catering services for high-speed railways: A three-echelon location routing model with tight time windows and time deadlines

An emerging task in catering services for high-speed railways (CSHR) is to design a distribution system for the delivery of high-quality perishable food products to trains in need. This paper proposes a novel model for integrating location decision making with daily rail catering operations, which are affected by various aspects of rail planning, to meet time-sensitive passenger demands. A three-echelon location routing problem with time windows and time budget constraints (3E-LRPTWTBC) is thus proposed toward formulating this integrated distribution system design problem. This model attempts to determine the capacities/locations of distribution centers and to optimize the number of meals delivered to stations. The model also attempts to generate a schedule for refrigerated cars traveling from distribution centers to rail stations for train loading whereby meals can be catered to trains within tight time windows and sold before a specified time deadline. By relaxing the time-window constraints, a relaxation model that can be solved using an off-the-shelf mixed integer programming (MIP) solver is obtained to provide a lower bound on the 3E-LRPTWTBC. A hybrid cross entropy algorithm (HCEA) is proposed to solve the 3E-LRPTWTBC. A small-scale case study is implemented, which reveals a 9.3% gap between the solution obtained using the HCEA and that obtained using the relaxation model (RM). A comparative analysis of the HCEA and an exhaustive enumeration algorithm indicates that the HCEA shows good performance in terms of computation time. Finally, a case study considering 156 trains on the Beijing-Shanghai high-speed corridor and a large-scale case study considering 1130 trains on the Chinese railway network are addressed in a comprehensive study to demonstrate the applicability of the proposed models and algorithm.     

Impacts of highly automated vehicles on travel demand: macroscopic modeling methods and some results

Automated vehicles (AV) will change transport supply and influence travel demand. To evaluate those changes, existing travel demand models need to be extended. This paper presents ways of integrating characteristics of AV into traditional macroscopic travel demand models based on the four-step algorithm. It discusses two model extensions. The first extension allows incorporating impacts of AV on traffic flow performance by assigning specific passenger car unit factors that depend on roadway type and the capabilities of the vehicles. The second extension enables travel demand models to calculate demand changes caused by a different perception of travel time as the active driving time is reduced. The presented methods are applied to a use case of a regional macroscopic travel demand model. The basic assumption is that AV are considered highly but not fully automated and still require a driver for parts of the trip. Model results indicate that first-generation AV, probably being rather cautious, may decrease traffic performance. Further developed AV will improve performance on some parts of the network. Together with a reduction in active driving time, cars will become even more attractive, resulting in a modal shift towards car. Both circumstances lead to an increase in time spent and distance traveled.

     

Optimizing train operational plan in an urban rail corridor based on the maximum headway function

The train operational plan (TOP) plays a crucial role in the efficient and effective operation of an urban rail system. We optimize the train operational plan in a special network layout, an urban rail corridor with one terminal yard, by decomposing it into two sub-problems, i.e., the train departure profile optimization and the rolling stock circulation optimization. The first sub-problem synthetically optimizes frequency setting, timetabling and the rolling stock circulation at the terminal without a yard. The maximum headway function is generated to ensure the service of the train operational plan without considering travel demand, then we present a model to minimize the number of train trips, and design a heuristic algorithm to maximize the train headway. On the basis of a given timetable, the rolling stock circulation optimization only involves the terminal with a yard. We propose a model to minimize the number of trains and yard–station runs, and an algorithm to find the optimal assignment of train-trip pair connections is designed. The computational complexities of the two algorithms are both linear. Finally, a real case study shows that the train operational plan developed by our approach enables a better match of train headway and travel demand, and reduces the operational cost while satisfying the requirement of the level of service.     

Long distance truck tracking from advanced point detectors using a selective weighted Bayesian model

Truck flow patterns are known to vary by season and time-of-day, and to have important implications for freight modeling, highway infrastructure design and operation, and energy and environmental impacts. However, such variations cannot be captured by current truck data sources such as surveys or point detectors. To facilitate development of detailed truck flow pattern data, this paper describes a new truck tracking algorithm that was developed to estimate path flows of trucks by adopting a linear data fusion method utilizing weigh-in-motion (WIM) and inductive loop point detectors. A Selective Weighted Bayesian Model (SWBM) was developed to match individual vehicles between two detector locations using truck physical attributes and inductive waveform signatures. Key feature variables were identified and weighted via Bayesian modeling to improve vehicle matching performance. Data for model development were collected from two WIM sites spanning 26 miles in California where only 11 percent of trucks observed at the downstream site traversed the whole corridor. The tracking model showed 81 percent of correct matching rate to the trucks declared as through trucks from the algorithm. This high accuracy showed that the tracking model is capable of not only correctly matching through vehicles but also successfully filtering out non-through vehicles on this relatively long distance corridor. In addition, the results showed that a Bayesian approach with full integration of two complementary detector data types could successfully track trucks over long distances by minimizing the impacts of measurement variations or errors from the detection systems employed in the tracking process. In a separate case study, the algorithm was implemented over an even longer 65-mile freeway section and demonstrated that the proposed algorithm is capable of providing valuable insights into truck travel patterns and industrial affiliation to yield a comprehensive truck activity data source.