Origin-destination Information Research Articles

Measuring health of highway network configuration against dynamic Origin-Destination demand network using weighted complex network analysis.

Ideal configuration or layout of highways should resemble the actual demands for the roads represented by Origin-Destination (OD) information. It would be beneficial if existing highways can be evaluated for their configurational fitness against the current demands, and newly planned highways can carefully be designed in terms of their layouts and topologies that would reflect the demands. Analysis techniques used for complex networks in the matured field of network theory can be applied for the highway layout health monitoring against the current OD information. This paper proposes a methodology of measuring the fitness of existing highways by comparing their structural configuration against conceptual OD networks using well-established techniques in network theory for complex networks. In the first phase, this paper conducts an empirical analysis and finds that both structural highway network and OD network follow the “power law” distribution as they are weighted by capacity and traffic volume respectively. It is also found that the power law coefficient of the OD network dynamically changes throughout the day and week. In the second phase, a noble methodology of weighting and measuring the health, of structural highway networks against OD networks by means of comparing their power law coefficients is proposed. It is found that the proposed method is effective at detecting deviations from ideal structural configurations associated with actual demands.

A Generalized Single-Level Formulation for Origin–Destination Estimation under Stochastic User Equilibrium

Origin–destination (OD) demand is an indispensable component for modeling transportation networks, and the prevailing approach to estimating OD demand using traffic data is through bi-level optimization. A bi-level optimization approach considering equilibrium constraints is computationally challenging for large-scale networks, which prevents the OD estimation (ODE) being scalable. To solve for ODE in large-scale networks, this paper develops a generalized single-level formulation for ODE incorporating stochastic user equilibrium (SUE) constraints. Two single-level ODE models are specifically discussed and tested. One employs a SUE based on the satisfaction function, and the other is based on the Logit model. Analytical properties of the new formulation are analyzed. The estimation methods are proven to be unbiased. Gradient-based algorithms are proposed to solve for this formulation. Numerical experiments are conducted on a small network and a large network, along with sensitivity analysis on sensor locations, historical OD information and measurement error. Results indicate that the new single-level formulation, in conjunction with the proposed solution algorithms, can achieve accuracy comparable with the bi-level formulation, while being much more computationally efficient for large networks.

Short-Term Origin-Destination Based Metro Flow Prediction with Probabilistic Model Selection Approach

Reliable prediction of short-term passenger flow could greatly support metro authorities’ decision processes, help passengers to adjust their travel schedule, or, in extreme cases, assist emergency management. The inflow and outflow of the metro station are strongly associated with the travel demand within metro networks. The purpose of this paper is to obtain such prediction. We first collect the origin-destination information from the smart-card data and explore the passenger flow patterns in a metro system. We then propose a data driven framework for short-term metro passenger flow prediction with the ability to utilize both spatial and temporal related information. The approach adopts two forecasts as basic models and then uses a probabilistic model selection method, random forest classification, to combine the two outputs to achieve a better forecast. In the experiments, we compare the proposed model with four other prediction models, i.e., autoregressive-moving-average, neural networks, support vector regression, and averaging ensemble model, as well as the basic models. The results indicate that the proposed approach outperforms the others in most cases. The origin-destination flows extracted from smart-card data can be successfully exploited to describe different metro travel patterns. And the framework proposed here, especially the probabilistic combination method, can improve the performance of short-term transportation prediction.

A New Method of Origin-Destination Survey Using License Plate Recognition by Recursive Traffic Zone Division

This paper proposes a new method of using License Plate Recognition (LPR) for Origin-Destination (OD) survey. LPR has shown many advantages in OD estimations such as lower average acquisition costs than other OD surveys, more information extracted from vehicles passing by LPR devices etc. We recommend a recursive method of traffic zone division as to flexibly divide traffic zones by network conditions or practical needs. In order to improve the speed of license plate matching process, we adopt an index structure of LPR database by the coding of license plates. Two important special OD cases, having-O-and-no-D and no-O-and-having-D are handled to supplement OD information by expanding the searching space-time scopes in database. The methodology is perfectly validated by an example of OD survey for a road network in CBD of Beijing.

Exploratory Method for Practitioners Analyzing the Impact of Integrated Fare Structures in Decentralized Metropolitan Regions

Metrolinx, the regional transportation agency tasked with improving the coordination and integration of all transportation modes in the Greater Toronto and Hamilton Area, has developed an exploratory method for analyzing the effects of new fare structures that integrate the fare systems of multiple transit service providers in the region. The method uses a data set of all weekday trips made in the region segmented by modes used and origin–destination information. A formula derived from the mode choice modeling theory is used to obtain fare elasticity based on unit cost, mode share, and time of day. The distribution of elasticities produced is then calibrated according to a literature review of fare elasticities, and in the future, it will be done according to local market research. The result is a spreadsheet-based tool that provides analysts with an ability to test more complex changes to fare systems, including testing fare integration between agencies and introducing fares by distance, mode, time of day, or a combination of those features. Exploratory in nature, the method is not a replacement for comprehensive market research or fare pilots. However, it addresses the shortcomings of traditional fare analyses that use only aggregate elasticities for diverse market segments by better reflecting the spectrum of transit user sensitivities associated with specific travel characteristics. Furthermore, it provides analysts with a straightforward tool to test the effects of complex fare structures more commonly used in Europe and Asia enabled by smart card and open payment technology on ridership, revenue, emissions, and social equity.

Travel demand corridors: Modelling approach and relevance in the planning process

In an ideal world, transportation networks and services would be adapted to the specific travel needs of each individual and would perfectly fit the corresponding desire lines (direct lines between origin and destination points). However, in practice, networks cannot be designed to accommodate each individual trip. Still, it is possible to optimize transportation systems from a collective demand point of view. To move from an individual to a collective scale, individual demands need to be encapsulated into demand corridors.Although current spatial tools and data mining techniques are able to identify corridors from numerous movements by using linear or non-linear trajectory data, their limitations—from a transportation point of view—include the use of non-intuitive parameters and the application of some aggregation processes that make it difficult to retrace the attributes of individual input data that could benefit the richness of the available data after processing. For that reason, we propose a new algorithm called Trajectory Clustering for Desire Lines (TraClus-DL), which can identify corridors from Origin-Destination (OD) information with simple parameters, such as spatial location, angles between lines, and sampling weights. The functionality of TraClus-DL as a diagnostic tool for transportation supply was assessed and tested using data from the 2013 OD travel survey conducted in the Montreal area. The sensitivity of the results, with respect to parameter settings, was evaluated, and a comparison with an existing algorithm was proposed.The results of this study demonstrate that transportation specialists can benefit from the convenience of using TraClus-DL as a corridor identification tool, which includes its potential to perform deep analyses at the corridor level. In addition, this study provides new insights into the possible uses of demand corridors as relevant tools for transportation planning, and in the decision-making processes in which a neutral reference is needed to evaluate how much the transportation supply differs from the collective travel demand.

ODCRep: Origin–Destination-Based Content Replication for Vehicular Networks

The evolution of vehicular network applications, from simple alert message exchange to more elaborate and sophisticated systems, boosts the need for content delivery solutions. A useful technique, in this case, is content replication, in which strategically selected vehicles replicate content and help in the delivery process. However, content replication is particularly challenging in vehicular networks, due to their special characteristics, such as highly dynamic topology, diverse density, and large-scale scenarios. Although there has been progress in routing and dissemination solutions for vehicular networks, few studies have concentrated on the content replication problem. To address this issue, we propose an origin–destination-based content replication (ODCRep) solution that focuses on balancing the number of replicas across the application area. Differently from existing solutions, ODCRep relies only on the origin–destination information and uses computationally efficient algorithms. Results from exhaustive simulations show that ODCRep can achieve high coverage, yet can also consume fewer resources than existing solutions.

Flow-based accessibility measurement: The Place Rank approach

In this paper, we empirically test the viability of a flow-based approach as an alternative to transport accessibility measurement. To track where commuters travel from and to (but not commute times), we use transactional smartcard data from residents in Singapore to construct the (daily) spatial network of trips generated. We use the Place Rank method to demonstrate the viability of the flow-based approach to study accessibility. We compute the Place Rank of each of 44 planning areas in Singapore. Interestingly, even though the spatial network is constructed using only origin–destination information, we find that the travel time of the trips out of each planning area generally decreases as the area’s Place Rank increases. The same is also the case for in-vehicle time, number of transfers in the network and transfer time. This shows that a flow-based approach can be used to measure the notion of accessibility, which is traditionally assessed using travel time information in the system. We also compare Place Rank with other indicators, namely, bus stop density, eigenvector centrality, clustering coefficient and typographical coefficient to evaluate an area’s accessibility. The results show that these indicators are not as effective as the Place Rank method.

Probability tree based passenger flow prediction and its application to the Beijing subway system

In order to provide citizens with safe, convenient and comfortable services and infrastructure in a metropolis, the prediction of passenger flows in the metro-net of subway system has become more important than ever before. Although a great number of prediction methods have been presented in the field of transportation, all of them belong to the station oriented approach, which is not well suited to the Beijing subway system. This paper proposes a novel metro-net oriented method, called the probability tree based passenger flow model, which is also based on historic origin-destination (OD) information. First it learns and obtains the appearance probabilities for each kind of OD pair. For the real-time origin datum, the destination datum is calculated, and then several kinds of passenger flow in the metro-net can be predicted by gathering all the contributions. The results of experiments, using the historical data of Beijing subway, show that although the proposed method has lower performance than existing prediction approaches for forecasting exit passenger flows, it is able to predict several additional kinds of passenger flow in stations and throughout the subway system; and it is a more feasible, suitable, and advanced passenger flow prediction model for Beijing subway system.

A Freeway Exit Volume Prediction Method Based on Traffic Origin-Destination Stability Pattern

AbstractThe traffic origin-destination (OD) data can be directly obtained from the freeway toll data. This information always reflects the vehicle status in temporal and spatial. And the OD information can also be used for making reasonable and effective predictions on the future trends of the freeway entrance volume. In this paper, an algorithm is proposed for predicting the freeway entrance volume based on the analysis of stability pattern of freeway traffic OD data. The contributions mainly include: (1) An information entropy-based method is presented for analyzing stability pattern of freeway traffic OD data; (2) Based on the stability pattern, a new prediction algorithm is applied on massive freeway entrance data. (3) The experimental results show that the proposed algorithm is reasonable and feasible, the accuracy is improved as well.

Urban Transit Coordination Using an Artificial Transportation System

An urban transit system usually consists of several modes, including busses, streetcars, a subway, and light rail. Unfortunately, coordination among different modes remains a challenging problem. Difficulties arise when modifying the transit network structure on a strategic level or when synchronizing timetables on a tactical level. Traditional transit network design and timetabling intend to solve a network-optimization problem based on static origin-destination (OD) information, with passenger assignment as a subproblem. In this paper, we propose an artificial urban transit system (AUTS) based on agent-based modeling and simulation. With AUTS, which is a special type of artificial transportation system (ATS), we are able to dynamically model the passenger's behavior and route choice and use the system to predict transit demand on a simplified transit network. The AUTS has the following important potential applications: forecasting transit flow; setting key parameters for urban transit networks - such as service frequencies and the capacity of subway trains - evaluating alternative modifications to subway rail and bus routes; and predicting the impact of special/emergency events to the transit network. We create a demonstration system of the Beijing transit network and present its applications in experiments.

Place Rank: Valuing Spatial Interactions

Accessibility measures the potential of opportunities for interaction. This paper proposes and explores a new flow-based measure, “place rank” using origin-destination information. Both impedance and value of opportunities are embedded in the dataset that includes the origin and destination of each person within the studied region. Individuals contribute to the place rank at their destination (work) zone with a power that depends on the attractiveness of the zone of origin. In this paper we demonstrate this place rank measure for three activities (Jobs, Resident Workers, and Health Services) in the Minneapolis-St. Paul metropolitan region and Jobs in Montreal, Canada. We compare place rank to traditional measures of accessibility. Since place rank is based on actual choices of origins and destinations it is a measure of realized rather than potential opportunities, and so unlike accessibility measures. Also it does not require the knowledge of travel time between all origins and destinations.

The Daily Commute: An Analysis of the Geography of the Labour Market using 2006 Census Data

This paper explores the major commuting areas within New Zealand and how commuting patterns have evolved between 1996 and 2006. It focuses primarily on the new insights that mapping and visualisation methods can bring to the analysis and understanding of complex flow data. In particular, we discuss some approaches to delineating labour market areas based on commuter inflow statistics and demonstrate the advantages that spider flow maps bring to the visualisation and understanding of commuting flows between areas. Spider flow maps are based on origin-destination information from the 2006 Census, but the paper also includes an historical perspective, examining changes in, the number and proportion of people commuting between areas and using different modes of transport used for commuting. Although our focus is on the advantages that these new methods can bring to the analysis of commuting data, some interesting findings arise. Both the number of commutes and the distance travelled by commuters has increased markedly since 1996, particularly around the largest cities of the Auckland. Wellington and Christchurch. Labour markets centered on these cities go well beyond territorial authority boundaries and. particularly in the Auckland case, are becoming increasingly polycentric. Data visucalisation makes the exploration of these patterns much more accessible.

Deriving origin–destination data from a mobile phone network

Acquiring high-quality origin-destination (OD) information for traffic in a geographic area is both time consuming and expensive while using conventional methods such as household surveys or roadside monitoring. These methods generally present only a snapshot of traffic situation at a certain point in time, and they are updated in time intervals of up to several years. A technique was developed that makes use of the global system for mobile communications (GSM) mobile phone network. Instead of monitoring the flow of vehicles in a transportation network, the flow of mobile phones in a cell-phone network is measured and correlated to traffic flow. This methodology is based on the fact that a mobile phone moving on a specific route always tends to change the base station nearly at the same position. For a first pilot study, a GSM network simulator has been designed, where network data can be simulated, which is then extracted from the phone network, correlated, processed mathematically and converted into an OD matrix. Primary results show that the method has great potential, and the results inferred are much more cost-effective than those generated with traditional techniques. This is due to the fact that no change has to be made in the GSM network, because the information that is needed can readily be extracted from the base station database, that is the entire infrastructure needed is already in place

Optimal freeway ramp control without origin–destination information

This paper develops an analytical framework for ramp metering, under which various ramp control strategies can be viewed as ramifications of the same most-efficient control logic with different threshold values, control methods, and equity considerations. The most-efficient control logic only meters the entrance ramps nearest critical freeway mainline sections so as to eliminate freeway internal queues, which is derived from a new formulation of the optimal ramp control problem. Instead of assuming the availability of real-time origin–destination information, the new formulation takes advantages of the stability and predictability of off-ramp exit percentages. Those properties of the off-ramp exit percentages are supported by empirical data, and allow us to formulate the optimal ramp control problem as a linear program whose input variables are all directly measurable by detectors in real-time. The solution is also tested on a real-world freeway section in a microscopic traffic simulator for demonstration. Time-dependent origin–destination tables and off-ramp exit percentages are compared as two alternative ways to represent the true real-time demand patterns that are important to freeway ramp metering.

Anonymous Vehicle Tracking for Real-Time Traffic Surveillance and Performance on Signalized Arterials

One of the fundamental requirements for facilitating implementation of any advanced transportation management and information system (ATMIS) is the development of a real-time traffic surveillance system able to produce reliable and accurate traffic performance measures. This study presents a new framework for anonymous vehicle tracking capable of tracing individual vehicles by the vehicle features. The core part of the proposed vehicle tracking method is a vehicle reidentification algorithm for signalized intersections based on inductive vehicle signatures. The new vehicle reidentification system consists of two major components: search space reduction and probabilistic pattern recognition. Not only real-time intersection performance but also intersection origin–destination information can be obtained as the algorithm’s basic output. A systematic simulation investigation was conducted of the performance and feasibility of anonymous vehicle tracking on signalized arterials using the Paramics simulation model. Extensive research experience with vehicle reidentification techniques on single roadway segments was the basis for investigating the performance that could be obtained from tracking individual vehicles across multiple detector stations. The findings of this study serve as a logical and necessary precursor to possible field implementation of vehicle reidentification techniques. The proposed anonymous vehicle tracking methodology with existing traffic surveillance infrastructure would be an invaluable tool for operating agencies in support of ATMIS strategies for congestion monitoring, adaptive traffic control, system evaluation, and provision of real-time traveler information.

Real‐Time OD Estimation Using Automatic Vehicle Identification and Traffic Count Data

A key input to many advanced traffic management operations strategies are origin–destination (OD) matricies. In order to examine the possibility of estimating OD matricies in real‐time, two constrained OD estimators, based on generalized least squares and Kalman filtering, were developed and tested. A one‐at‐a‐time processing method was introduced to provide an efficient organized framework for incorporating observations from multiple data sources in real‐time. The estimators were tested under different conditions based on the type of prior OD information available, the type of assignment available, and the type of link volume model used. The performance of the Kalman filter estimators also was compared to that of the generalized least squares estimator to provide insight regarding their performance characteristics relative to one another for given scenarios. Automatic vehicle identification (AVI) tag counts were used so that observed and estimated OD parameters could be compared. While the approach was motivated using AVI data, the methodology can be generalized to any situation where traffic counts are available and origin volumes can be estimated reliably. The primary means by which AVI data was utilized was through the incorporation of prior observed OD information as measurements, the inclusion of a deterministic link volume component that makes use of OD data extracted from the latest time interval from which all trips have been completed, and through the use of link choice proportions estimated based on link travel time data. It was found that utilizing prior observed OD data along with link counts improves estimator accuracy relative to OD estimation based exclusively on link counts.

Simple Decentralized Feedback Strategies for Route Guidance in Traffic Networks

The paper presents a feedback route guidance strategy for complex, meshed traffic networks. Essential components of the strategy are simple, decentralized control laws of the bang-bang, P, or PI types that may be designed based on trial-and-error. Simulation investigations demonstrate the efficiency of the proposed strategy for two example networks under several scenarios of demand and incident conditions. Feedback route guidance, though exclusively based on measurable instantaneous travel times (no predictions, no demand nor origin–destination information are provided), is shown to equalize experienced travel times along any couple of used alternative routes in the network, and to considerably reduce travel delays compared to the no-control case.

Using Origin-Destination Analysis in Marketing Tourism for Large Natural Regions

Although origin-destination information is often obtained in tourism marketing studies, the data are often analyzed in a format too general to be most useful for promotional purposes. This study utilizes visitor residence data to determine tourist destination subregions of a large natural region, the Adirondack Park in upstate New York. It then classifies, visitors to these subregions by geographic residence regions corresponding to these receiving primary media coverage. This provides local communities within the Park information on which neighbouring communities have the same primary media residence regions, for which cooperative subregional promotional strategies would be feasible, and which media residence regions should receive their scarce promotional funds.