A distributed origin–destination demand estimation approach for real-time traffic network management

Abstract

This paper describes a distributed recursive heuristic approach for the origin–destination demand estimation problem for real-time traffic network management applications. The distributed nature of the heuristic enables its parallelization and hence reduces significantly its processing time. Furthermore, the heuristic reduces dependency on historical data that are typically used to map the observed link flows to their corresponding origin–destination pairs. In addition, the heuristic allows the incorporation of any available partial information on the demand distribution in the study area to improve the overall estimation accuracy. The heuristic is implemented following a hierarchal multi-threading mechanism. Dividing the study area into a set of subareas, the demand of every two adjacent subareas is merged in a separate thread. The merging operations continue until the demand for the entire study area is estimated. Experiments are conducted to examine the performance of the heuristic using hypothetical and real networks. The obtained results illustrate that the heuristic can achieve reasonable demand estimation accuracy while maintaining superiority in terms of processing time.     

Utility,entropy and a “paradox” of traffic flow

The relation between various deviations of the logit based route choice model and the associated benefit measures are explored in the light of “another” paradox of traffic flow highlighted recently by Sheffi and Daganzo.     

Non‐physical barriers to traffic flow and the PTA programme of action: problems and prospects

A lot of resources have .been committed to the provision of transport infrastructure in Eastern and Southern Africa. Some of these resources have been borrowed from outside and have to be repaid. Hence the need to maximize their use. However, this is being minimized by non‐infrastructural bottlenecks to traffic flow. These range from macro‐economic constraints with implications for passenger (bus) transport such as the non‐availability or acute shortage of foreign exchange to slow, cumbersome documentation procedures particularly for cross‐border traffic.

The Preferential Trade Area (PTA) for Eastern and Southern Africa is geared to the reduction and eventual elimination of actual and potential non‐physical barriers to traffic flow, for which it has instituted a number of schemes. After introductory remarks on the sub‐regional economy, its transport sector and avenues for sub‐regional cooperation and the implementation of these schemes is assessed and other non‐physical barriers yet to be addressed by the PTA are examined before conclusions are drawn.     

A multi-commodity Lighthill–Whitham–Richards model of lane-changing traffic flow

Systematic lane changes can seriously deteriorate traffic safety and efficiency inside lane-drop, merge, and other bottleneck areas. In our previous studies (Jin, 2010a, Jin, 2010b), a phenomenological model of lane-changing traffic flow was proposed, calibrated, and analyzed based on a new concept of lane-changing intensity. In this study, we further consider weaving and non-weaving vehicles as two commodities and develop a multi-commodity, behavioral Lighthill–Whitham–Richards (LWR) model of lane-changing traffic flow. Based on a macroscopic model of lane-changing behaviors, we derive a fundamental diagram with parameters determined by car-following and lane-changing characteristics as well as road geometry and traffic composition. We further calibrate and validate fundamental diagrams corresponding to a triangular car-following fundamental diagram with NGSIM data. We introduce an entropy condition for the multi-commodity LWR model and solve the Riemann problem inside a homogeneous lane-changing area. From the Riemann solutions, we derive a flux function in terms of traffic demand and supply. Then we apply the model to study lane-changing traffic dynamics inside a lane-drop area and show that the smoothing effect of HOV lanes is consistent with observations in existing studies. The new theory of lane-changing traffic flow can be readily incorporated into Cell Transmission Model, and this study could lead to better strategies for mitigating bottleneck effects of lane-changing traffic flow.     

Speed or spacing? Cumulative variables,and convolution of model errors and time in traffic flow models validation and calibration

This paper proves that in traffic flow model calibration and validation the cumulative sum of a variable has to be preferred to the variable itself as a measure of performance. As shown through analytical relationships, model residuals dynamics are preserved if discrepancy measures of a model against reality are calculated on a cumulative variable, rather than on the variable itself. Keeping memory of model residuals occurrence times is essential in traffic flow modelling where the ability of reproducing the dynamics of a phenomenon – as a bottleneck evolution or a vehicle deceleration profile – may count as much as the ability of reproducing its order of magnitude. According to the aforesaid finding, in a car-following models context, calibration on travelled space is more robust than calibration on speed or acceleration. Similarly in case of macroscopic traffic flow models validation and calibration, cumulative flows are to be preferred to flows. Actually, the findings above hold for any dynamic model.     

A GIS‐based traffic analysis zone design: implementation and evaluation

The purpose of this paper is to implement an efficient method for GIS‐based traffic analysis zone (TAZ) design in order to evaluate and validate such a method. The method was developed by the authors.

Moran's I spatial autocorrelation coefficient and sample variance are used for evaluating the generated TAZs using the Champaign‐Urbana, IL region as a case study. Sensitivity analysis is also conducted to explore the fluctuations in TAZ generation outcomes. The evaluation, the validation as well as the TAZ design have been implemented with ARC/INFO GIS software on a UNIX workstation platform.     

The Hamilton–Jacobi partial differential equation and the three representations of traffic flow

This paper applies the theory of Hamilton–Jacobi partial differential equations to the case of first-order traffic flow models. The traffic flow surface is analyzed with respect to the three 2-dimensional coordinate systems arising in the space of vehicle number, time and distance. In each case, the solution to the initial and boundary value problems are presented. Explicit solution methods and examples are shown for the triangular flow-density diagram case. This unveils new models and shows how a number of existing models are cast as special cases.     

A GIS‐based traffic analysis zone design: technique

The main purpose of this paper is to develop an efficient method to design traffic analysis zones (TAZs), which is necessary for implementing a planning process with Geographic Information System (GIS) for Transportation (GIS‐T), using statistical spatial data analyses and GIS technology. The major roles of GIS in this method are: (1) to produce basic spatial units (BSUs) with topological data structure; (2) to integrate various procedures during the TAZ generation including computer program routines; and (3) to visualize the output of each TAZ generation. One of the most significant reasons for obtaining well‐defined TAZs is the fact that they are defined at the outset of transportation demand modeling, used from trip generation to trip assignment, and will ultimately affect transportation policy decisions.

Toward obtaining well‐defined TAZs, this paper concentrates on two important constraints: homogeneity and contiguity. Iterative partitioning technique is adopted to promote the optimum homogeneity of generated TAZs, while a contiguity checking algorithm is developed to ensure contiguous TAZs are generated by the iterative partitioning technique.     

A method to estimate trip O‐D patterns using a neural network approach

This paper proposes a method which identifies the trip origin‐destination (O‐D) matrix when many pairs of values for the right hand side column (B) and the bottom row (A) of the matrix are given. The method considers B and A as the cause (input) and effect (output) of a system, respectively, and that the O‐D matrix represents the relationship between the cause and the effect. The relationship which satisfies all pairs of the cause and the effect data exactly may not be identified, but, should a general pattern of the relationship exist, it should emerge when many data sets of B and A are given. Two steps are involved in the method: the first step examines if a consistent O‐D pattern exists; if a pattern is found to exist, the second step identifies the values of the elements of the O‐D matrix. The first step is based on the shape of the possibility distributions of the values of the matrix elements. The second step uses a simple back‐propagation neural network. The method is useful to problems that require identification of the cause‐effect relationship when many sets of data for the cause and effect are available, for example, the station‐to‐station travel pattern on a rapid transit line when the total entering and exiting passengers are known at each station for many different days. The model can also be applied to other transportation problems which involve input and output relation.     

Modelling users’ behaviour in inter-urban carsharing program: A stated preference approach

In this paper, the effects of a inter-urban carsharing program on users’ mode choice behaviour were investigated and modelled through specification, calibration and validation of different modelling approaches founded on the behavioural paradigm of the random utility theory. To this end, switching models conditional on the usually chosen transport mode, unconditional switching models and holding models were investigated and compared. The aim was threefold: (i) to analyse the feasibility of a inter-urban carsharing program; (ii) to investigate the main determinants of the choice behaviour; (iii) to compare different approaches (switching vs. holding; conditional vs. unconditional); (iv) to investigate different modelling solutions within the random utility framework (homoscedastic, heteroscedastic and cross-correlated closed-form solutions). The set of models was calibrated on a stated preferences survey carried out on users commuting within the metropolitan area of Salerno, in particular with regard to the home-to-work trips from/to Salerno (the capital city of the Salerno province) to/from the three main municipalities belonging to the metropolitan area of Salerno. All of the involved municipalities significantly interact each other, the average trip length is about 30 km a day and all are served by public transport. The proposed carsharing program was a one-way service, working alongside public transport, with the possibility of sharing the same car among different users, with free parking slots and free access to the existent restricted traffic areas. Results indicated that the inter-urban carsharing service may be a substitute of the car transport mode, but also it could be a complementary alternative to the transit system in those time periods in which the service is not guaranteed or efficient. Estimation results highlighted that the conditional switching approach is the most effective one, whereas travel monetary cost, access time to carsharing parking slots, gender, age, trip frequency, car availability and the type of trip (home-based) were the most significant attributes. Elasticity results showed that access time to the parking slots predominantly influences choice probability for bus and carpool users; change in carsharing travel costs mainly affects carpool users; change in travel costs of the usually chosen transport mode mainly affects car and carpool users.     

Short‐term traffic forecasting: Overview of objectives and methods

In the last two decades, the growing need for short‐term prediction of traffic parameters embedded in a real‐time intelligent transportation systems environment has led to the development of a vast number of forecasting algorithms. Despite this, there is still not a clear view about the various requirements involved in modelling. This field of research was examined by disaggregating the process of developing short‐term traffic forecasting algorithms into three essential clusters: the determination of the scope, the conceptual process of specifying the output and the process of modelling, which includes several decisions concerning the selection of the proper methodological approach, the type of input and output data used, and the quality of the data. A critical discussion clarifies several interactions between the above and results in a logical flow that can be used as a framework for developing short‐term traffic forecasting models.     

Can passenger flow distribution be estimated solely based on network properties in public transport systems?

Transportation - We present a pioneering investigation into the relation between passenger flow distribution and network properties in public transport systems. The methodology is designed in a...     

A note on “highway pricing and capacity choice in a road network under a build-operate-transfer scheme”

This note rectifies an error in the paper by Yang and Meng (2000) on highway pricing and capacity choice, and shows that under essentially the same assumptions as for Mohring and Harwitz (1962) and Strotz (1964), self-financing applies to a general network.     

Energy consumption in China’s logistics industry: A decomposition analysis using the LMDI approach

The logistics industry plays a critical role in boosting China’s economic development, although at significant externality costs. Using the 1980–2010 data, we examine the historical evolution of energy consumption in China’s logistics industry. The logarithmic mean Divisia index (LMDI) method was used to analyze the key factors that drove the chronicle changes in logistics energy consumption in China. Changes in energy consumption of China’s logistics industry are attributed to growth in logistics activity, modal shift in freight transportation, increases in transport intensity, and overall improvements in energy intensity: (1) China’s logistics industry enjoyed fast growth with an average annual growth rate of 9.65% from 1980 to 2010. Increase in logistics activity has been the major force driving up logistics energy consumption (accounting for 48.8% of logistics energy increase); (2) Logistics energy consumption increased by 22.91 times, averaging at 11.9% growth per year. Fuel consumption in highway transportation has become more dominant in logistics energy consumption; (3) While changes in logistics activities, transportation modes and transport intensity pushed up logistics energy consumptions, improvements in energy intensity helped significantly to curb the energy rise in China’s logistics industry.     

Finding optimal solutions for vehicle routing problem with pickup and delivery services with time windows: A dynamic programming approach based on state–space–time network representations

Optimization of on-demand transportation systems and ride-sharing services involves solving a class of complex vehicle routing problems with pickup and delivery with time windows (VRPPDTW). This paper first proposes a new time-discretized multi-commodity network flow model for the VRPPDTW based on the integration of vehicles’ carrying states within space–time transportation networks, so as to allow a joint optimization of passenger-to-vehicle assignment and turn-by-turn routing in congested transportation networks. Our three-dimensional state–space–time network construct is able to comprehensively enumerate possible transportation states at any given time along vehicle space–time paths, and further allows a forward dynamic programming solution algorithm to solve the single vehicle VRPPDTW problem. By utilizing a Lagrangian relaxation approach, the primal multi-vehicle routing problem is decomposed to a sequence of single vehicle routing sub-problems, with Lagrangian multipliers for individual passengers’ requests being updated by sub-gradient-based algorithms. We further discuss a number of search space reduction strategies and test our algorithms, implemented through a specialized program in C++, on medium-scale and large-scale transportation networks, namely the Chicago sketch and Phoenix regional networks.     

Are travelers substituting between transportation network companies (TNC) and public buses? A case study in Pittsburgh

Transportation - Transportation network companies (TNC) provide mobility services that are influencing travel behavior in unknown ways due to limited TNC trip-level data. How they interact with...     

A discrete dynamical system of formulating traffic assignment: Revisiting Smith’s model

Through relaxing the behavior assumption adopted in Smith’s model (Smith, 1984), we propose a discrete dynamical system to formulate the day-to-day evolution process of traffic flows from a non-equilibrium state to an equilibrium state. Depending on certain preconditions, the equilibrium state can be equivalent to a Wardrop user equilibrium (UE), Logit-based stochastic user equilibrium (SUE), or boundedly rational user equilibrium (BRUE). These equivalence properties indicate that, to make day-to-day flows evolve to equilibrium flows, it is not necessary for travelers to choose their routes based on actual travel costs of the previous day. Day-to-day flows can still evolve to equilibrium flows provided that travelers choose their routes based on estimated travel costs which satisfy these preconditions. We also show that, under a more general assumption than the monotonicity of route cost function, the trajectory of the dynamical system converges to a set of equilibrium flows by reasonably setting these parameters in the dynamical system. Finally, numerical examples are presented to demonstrate the application and properties of the dynamical system. The study is helpful for understanding various processes of forming traffic jam and designing an algorithm for calculating equilibrium flows.