Origin-destination Trip Matrix Research Articles

Automation of Collection of Primary Data for Development of a Passenger Origin-Destination Trip Correspondence Matrix Based on Computer Vision and Neural Network Technologies

The origin-destination trip matrix is a fundamental characteristic of a transport network, and development of a reliable correspondence matrix is the most important task in organising passenger traffic. It is the basis on which the public transport route network of a city (region) is built and optimised.Currently, collection of initial information for construction of a travel correspondence matrix is carried out through field surveys comprising questionnaire surveys of the population; accounting for movement of passengers according to the coupons issued to them; checkers, tellers manually counting passengers in vehicle compartments; simple surveys of passengers. Besides, mathematical modelling is used based on statistical data on the number of residents in various districts of the city, employees in enterprises and students in educational institutions, as well as on available data on the characteristics of passenger traffic along certain routes. All these surveys are very expensive and are carried out once over few years; they give a large error, which is why decisions made on the basis of these data are far from being optimal.There are a lot of solutions in the software and hardware market that provide automated collection of data on passenger flows. They are based on the use of infrared sensors or of video recording. However, none of these systems provide information about the points of entry and exit of each passenger. The objective of this study was to develop methods for automating the collection of reliable information about passenger trips, that will be the base for building up-to-date and reliable passenger trip correspondence matrices. This task can be solved by constant monitoring of passengers’ trips with fixing places of entry and exit of each passenger.The study describes the possibility of creating software based on computer vision and artificial intelligence which will provide automation of collection of primary information about travel of each passenger from the place of boarding into the vehicle to exit from it, that is, automation of data generation to build a passenger trip correspondence matrix.

How comparable are origin-destination matrices estimated from automatic fare collection, origin-destination surveys and household travel survey? An empirical investigation in Lyon

Abstract Origin–destination (OD) matrices are one of the key elements in travel behaviour analysis. For decades, transportation researchers have mostly used data obtained by active solicitation such as surveys to construct these matrices but new data sources like automatic fare collection (AFC) are now available and can be used to measure OD flows. As a result, a more heterogeneous corpus of data sources is now available to estimate travel demand. However, little research examines how comparable the estimated demands may be. In this paper, three data sources namely a household travel survey, a large scale origin–destination survey and entry only automated fare collection are processed to derive typical weekday public transit OD trip matrices. Various elements of the resulting matrices are then compared. While all the matrices share some common characteristics, there are also substantial differences that must be addressed. AFC data is not error-free and needs to be supplemented with data from other sources to construct a representative OD trip matrix. This is because not all destinations can be inferred, the smart card penetration rate is far less than 100% and fare evasion cannot be ignored. Our empirical results suggest that scaling an AFC matrix with automated passenger counts may be a viable solution. The results also indicate that the household travel survey significantly underestimates the volume of public transit trips compared to the other sources. The findings of this research contribute to a better understanding of the available data sources for public transit demand estimation. They can help practitioners to improve the quality and accuracy of OD matrices.

ADJUSTMENT OF A TYPE OF TRAFFIC FUNCTION FOR CALCULATION OF ORIGIN-DESTINATION TRANSIT FLOWS ACROSS THE CENTRAL PART OF THE CITY

Problem. The article refers to the problem of the concentration of traffic flows in the central business part of a large city. The transport problem in the city center can be solved by the creation of transport ring roads. To do this, it is necessary to know the capacity of the sections of the ring road, and for this purpose it is necessary to calculate traffic flows along the ring. To do this, we use a gravitational model for calculating the origin-destination trip matrix. This matrix is needed for future traffic assignment on the constructed transport ring. There are several types of transport gravity functions proposed by the different authors and we need to choose the right one or create a new type of the function to solve the current task. Goal. The goal is to suggest a criterion for choosing the optimal form of transport gravity function in a given problem. Methodology. We use the gravity model of transport trip distribution along the road network and the Shatsky-Sheleyhovsky calculation procedure. The proposed criteria for choosing the proper gravity function is the sum of absolute values of the differences between the sum of origin-destination trip values for certain destination mega-region and the given arriving capacity of this mega-region. Results: A criterion for choosing the optimal form of transport gravity function is developed. Several forms of gravity function were considered and estimated by the proposed criteria. The best type of the gravity function for the given task is the quadratic function. With increasing travel distance, the motivation for using individual transport also increases. Originality. It is shown that multiplying the function of transport gravitation by a constant (except zero) does not affect the calculation of the matrix. Practical value. The obtained results are used in the developed method of calculating the required capacity of the sections of the transport ring, which can be constructed in a large city to reduce the traffic load on the central part of the city. This is explained by the fact that for those who drive their own cars and make trips in the morning and in the evening, the travel factor is not the most defining factor in choosing the job, as opposed to those who use the public transport

Application of smart card data in validating a large-scale multi-modal transit assignment model

The accuracy of transit assignment plays an important role in the successful design and operation of a transit system. The majority of previous studies on validating transit assignment models has used limited survey data or has lacked a large-scale multimodal and high quality dataset. Considering the advantages of smart card [automatic fare collection (AFC)] systems, the aims of this study are to put forward a methodological framework to validate existing transit assignment models and to quantify the performance of these models. Our study combines data from three sources: the General Transit Feed Specification, an AFC system, and a strategic transport model from a large-scale multimodal public transport network, namely the South-East Queensland (SEQ) network in Australia. The AFC system in SEQ has provided a very large and highly accurate dataset on passenger boardings and alightings for the three transit modes of bus, rail and ferry. Following a data analysis, an origin–destination trip matrix is estimated for the AM peak period using AFC data as an input to the transit assignment model. Then, the results of the transit assignment model are compared with the actual passengers’ route choices over the same period, at different levels of aggregation. The model performance is quantified by each route (and direction), by each segment of each route (and direction), and by each stop. The results indicate that relatively tighter thresholds are required to validate the transit assignment at the segment level than at the stop level. Furthermore, the validation results indicate that the greatest error is realized for the bus mode, while the level of accuracy in the rail mode is the best. The results suggest a segment-level analysis should be used as the most useful level of aggregation for future calibration and validation of transit assignment models.

Design of Integrated Limited-Stop and Short-Turn Services for a Bus Route

As a major choice for daily travel, public transit plays an important role in transporting passengers, thus relieving congestion on urban transit routes. In high-demand bus networks, urban transit demand presents imbalance of use of urban bus corridors. The demand patterns in both directions are asymmetric. In this paper, we develop a model which calculates the network and transportation costs in terms of wait time, in-vehicle travel time, and operator costs. We propose an integrated strategy, with an integrated limited-stop and short-turn line, by adjusting a variety of frequencies to meet the unbalanced and asymmetric demand. To minimize these costs, a model with a genetic algorithm can determine frequencies and the proper stations which can be skipped, as well as where turning back can occur, given an origin-destination trip matrix. Numerical examples are optimized to test the availability of an integrated service by minimizing the objective function, and the results are analyzed. Our results show that integrated service patterns can be adjusted to meet the demand under different conditions. In addition, the optimized schemes of an integrated service and the frequencies derived from the model can significantly reduce total cost.

Integrated Determination of Network Origin–Destination Trip Matrix and Heterogeneous Sensor Selection and Location Strategy

This paper proposes a two-stage optimization model to determine the origin–destination (O–D) trip matrix and the heterogeneous sensor deployment strategy in an integrated manner for a vehicular traffic network using sensor information from active (camera-based license plate recognition) and passive (vehicle detector) sensors. The first stage solves the heterogeneous sensor selection and location problem to determine the optimal sensor deployment strategy, in terms of the selection of the numbers of the two sensor types and their installation locations, to maximize the traffic information available for the O–D matrix estimation problem. The traffic information includes the observed link flow, path trajectory, and path coverage information. The second stage leverages this traffic information to determine the network O–D matrix that minimizes the error between the observed and estimated traffic flows (link, O–D, and/or path). Correspondingly, two network O–D matrix estimation models are proposed where the link-based model incorporates the flow conservation rule between O–D and link flows and uses the link-node incidence matrix, and the path-based model assumes a given link-path incidence matrix. An iterative solution procedure is designed to determine the network O–D matrix and link flow estimates. Results from numerical experiments suggest that the path-based model outperforms the link-based model in the estimation of network O–D matrices. The relative contributions of combinations of the two sensor types to the network O–D matrix estimation problem are also analyzed. They suggest that active sensors provide valuable path information to solve the O–D matrix estimation problem, but at the cost of a significantly higher unit price. The study results have key implications for heterogeneous sensor selection and location strategies.

Estimation of Origin-Destination Trip Matrices for Small Cities

This paper presents a model of data assessment for the requirements of a classical four-step model of traffic demand in individual traffic in small cities. The procedure is carried out by creating an initial origin-destination trip matrix using data from the traffic count and by defining the average rate of trip generation within single households. The research applied fuzzy logic for the correction of the initial trip matrix. The paper also presents the recommendations for defining the borders of traffic zones, as well as the locations of traffic counts. A flowchart has been used to show a summarized presentation of the proposed model. In the last part of the paper the model was tested on an example of a smaller city in the Republic of Croatia.

Design of limited-stop services for an urban bus corridor with capacity constraints

In high-demand bus networks, limited-stop services promise benefits for both users and operators, and have proven their attractiveness in systems such as Transmilenio (Bogota, Colombia) and Transantiago (Santiago, Chile). The design of these services involves defining their itinerary, frequency and vehicle size, yet despite the importance of these factors for the network’s efficiency, no published works appear to provide the tools for designing high-frequency unscheduled services on an urban bus corridor, minimizing social costs. This paper presents an optimization approach that minimizes these costs in terms of wait time, in-vehicle travel time and operator cost. Various optimization models are formulated that can accommodate the operating characteristics of a bus corridor, given an origin–destination trip matrix and a set of services that are a priori attractive. The models then determine which of these services should be offered at what frequencies and with which type of vehicles. A case study in which the model is applied to a real-world case of a bus corridor in the city of Santiago, Chile, is presented and the results are analyzed. Finally, the model is used on two different demand scenarios establishing which type of services tend to be good candidates on each case and providing preliminary insights on the impact of some key parameters.

Optimal traffic plate scanning location for OD trip matrix and route estimation in road networks

During the last decade, there has been a substantial interest in how to determine the optimal number and locations of traffic counters for origin–destination (OD) trip matrices estimation. On the contrary, the optimal allocation of plate scanning devices has received very limited attention, even though several authors have demonstrated that plate scanning (route identification) techniques are much more informative than those based on traditional link count information. This paper provides techniques for obtaining the optimal number and location of plate scanning devices for a given prior OD distribution pattern under different situations, i.e. maximum route identifiability or budget constraints. Two rules analogous to the counting location problem are developed, and several integer linear programming models fulfilling these rules are proposed. The proposed methods are finally illustrated by their application into Nguyen–Dupuis and Cuenca networks.

Capacitated Demand Changes as a Project Evaluation Measure

Given a transport network and fixed demand (expressed as an origin-destination trip matrix), “capacitated demand” is defined as the portion of the given demand that can be served by the network without violating capacity constraints on any of the network’s facilities. A significant performance measure of a proposed project in a congested area is the amount of change in the capacitated demand, which is caused by the project: the bigger the change, the bigger the contribution of the project to the productivity of the network and its ability to serve the transport needs. Recently, a modified procedure for the evaluation of transport projects (the Pratt procedure) has been implemented in Israel. It uses capacitated demand as a major evaluation measure. The capacitated demand measure and methods for its estimation are described, as are some of the unique features of Pratt and how the new measure is incorporated into it.

An analysis of the reliability of an origin-destination trip matrix estimated from traffic counts

In recent years a large number of models have been developed to estimate an origin-destination (OD) trip matrix from link traffic count data. A key objective of the models is to guarantee the quality of the estimated OD matrix. Because traffic counts alone are generally not enough to identify the true OD matrix, it is important to evaluate the estimation accuracy of the model. This paper presents a theoretical investigation into the reliability of an estimated OD trip matrix employing the concept of “Maximum Possible Relative Error” (MPRE). It is shown that the MPRE can be formulated as a simple quadratic programming problem. The proposed MPRE shows the upper bound of the real relative error for a particular fitted OD matrix and plays a useful role in the analysis of the reliability of the estimated OD trip matrix.