Origin Destination Research Articles

High Dimensional Origin Destination Calibration Using Metamodel Assisted Simultaneous Perturbation Stochastic Approximation

The huge traffic data generated by intelligent transportation system (ITS) leads to the development of many advanced traffic models. These traffic models consist of many adjustable parameters which need to be calibrated before they are used in practice. This paper focuses on the offline calibration of origin-destination (OD) input parameters of a simulation-based traffic model to match its output with sensor data. An improved Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm called Metamodel Assisted Simultaneous Perturbation Stochastic Approximation (MSPSA) is proposed in this paper to calibrate high-dimensional OD parameters within a tight computational budget. The proposed MSPSA combines the gradient of SPSA with the gradient of a differentiable metamodel function to improve the calibration efficiency. An integer program is also used to fine-tune the OD estimates. The proposed MSPSA algorithm is tested on a simple synthetic toy network and complex road network of Kuala Lumpur (KL), Malaysia. The proposed MSPSA algorithm is compared against SPSA and state-of-the-art Weighted Simultaneous Perturbation Stochastic Approximation (WSPSA) in both transportation networks. For KL network, synthetic and real-world sensor measurements are used as ground truth references to evaluate the performance of each approach. Based on the simulation results, the proposed MSPSA algorithm is able to gain at least 50% of improvement as compared to SPSA and WSPSA in both synthetic and real-world scenarios.

Privacy Protection Method for Cellular Signaling Data Based on Genetic Algorithm

Cellular signaling data (CSD) is a new data source of intelligent transportation and has great potential in the study of human mobility. However, research recently has shown that the privacy of individual trajectories of travelers can be easily leaked due to their unique travel patterns, which will lead to the disclosure of a traveler’s trajectory privacy. To address this issue, the risk of privacy leakage in a CSD data set was quantitatively measured due to reidentification attacks. To reduce the risk of trajectory privacy leakage, a spatial generalization method based on an improved genetic algorithm (IGA) was proposed. We transformed the base station aggregation problem that meets the k-anonymity requirement into a shortest path problem. The data utility loss after privacy preservation was evaluated based on origin–destination (OD) flow analysis of traffic analysis zones (TAZs). Finally, the relationships between privacy preservation and data utility were revealed. In all of the cases, more privacy protection corresponded to less data utility. The discovered relationships provide useful guidance for data publishers on how to choose the right tradeoff between privacy protection and data utility when publishing or sharing such sensitive data.

Parallel and Collaborative Passenger Flow Control of Urban Rail Transit Under Comprehensive Emergency Situation

The artificial cooperative control methods are used to fully utilize the train capacity and reduce the passenger delays by coordinating the number of entering and boarding passengers at each station, but it ignores the potential risk associated with the aggregation attributes of passengers in the station under emergency and the unfairness caused by the imbalance of travel opportunities for different origin–destination (OD) passengers. A new parallel and cooperative control model under comprehensive emergency situation is constructed from the perspective of guaranteeing travel safety, improving travel fairness, and increasing travel efficiency to generate passenger flow control strategies. The model aims to maximize passenger turnovers, minimize passenger delays and reduce the difference in the ratio of stranded passengers at stations. The complex multi-objective integer programming problem involving nonlinear equation constraints is difficult to solve numerically. A dynamic programming model taking train sequence as stage variable is then constructed as well as an NSGA-II algorithm with multiple improved strategies (MIS-NSGA-II) is proposed to solve the model. Numerical experiments on a line of Beijing Rail Transit show that the MIS-NSGA-II outperforms the traditional NSGA-II, and that increasing the overload coefficient of the train and the tolerance coefficient of each area in the station can further alleviate passenger congestion and promote the operation efficiency. Compared with other control strategies mentioned in this study, the proposed control model from a comprehensively perspective, which can ensure the travel safety and promote the travel efficiency while forcefully decongesting the bottleneck areas and improving the fairness of passenger travel.

The potential of Wi-Fi data to estimate bus passenger mobility

Last decades have been marked by deep socio-economic transformations, an uneven evolution of transport demand in main urban areas and the emergence of new and more sustainable modes of transportation (carpooling, self-services bicycles). These changes have strongly impacted the interaction between service supply and demand in the transport industry. In this context, passive data as Wi-Fi and Bluetooth become a key source of information to understand individual mobility behaviors and ensure the sustainable development of transport infrastructures. In this paper, we present a framework that uses disruptive technology to collect passive data in buses, continuously and at a lower cost than traditional mobility surveys. Previous research, conducted over a more limited spatial and temporal framework, uses filtering methods, which do not allow the results to be replicated. This study uses artificial intelligence to sort transmitted signals, get transit ridership and build Origin–Destination matrices. Its originality consists in providing a concrete, automatic and replicable method to transport operators. The comparison of the results with other data sources confirms the relevance of the presented algorithms in demand forecasting. Therefore, our findings provide interesting insights for data-driven decision making and service quality management in urban public transport.

Msp1-mediated proofreading mechanism for localization of tail-anchored membrane proteins.

Protein targeting to organelles has been thought to be a very precise process, and proteins that fail to localize correctly are rapidly degraded. Tail-anchored proteins are posttranslationally targeted to the endoplasmic reticulum membrane via guided entry of tail-anchored (TA) proteins pathway. However, these proteins can be mislocalized to the mitochondrial outer membrane. We found that the AAA-ATPase Msp1 on the mitochondrial outer membrane extracts mislocalized TA proteins to the cytosol, passing them to the guided entry of the TA proteins pathway to facilitate their transfer to the endoplasmic reticulum membrane. After the transfer to the endoplasmic reticulum, such TA proteins are directed to degradation if they are recognized by the quality control system on the endoplasmic reticulum. If not recognized, they are retargeted to their original destination along the secretory pathway. Thus, we have identified an intracellular proofreading system that corrects the localization of TA proteins.

A hybrid solution method for the bi-objective traffic counting location problem using previous origin–destination trip tables

A hybrid solution method for the bi-objective traffic counting location problem using previous origin–destination trip tables

Medium-term forecast of daily passenger volume of high speed railway based on DLP-WNN

PurposeThis paper aims to propose a medium-term forecast model for the daily passenger volume of High Speed Railway (HSR) systems to predict the daily the Origin-Destination (OD) daily volume for multiple consecutive days (e.g. 120 days).Design/methodology/approachBy analyzing the characteristics of the historical data on daily passenger volume of HSR systems, the date and holiday labels were designed with determined value ranges. In accordance to the autoregressive characteristics of the daily passenger volume of HSR, the Double Layer Parallel Wavelet Neural Network (DLP-WNN) model suitable for the medium-term (about 120 d) forecast of the daily passenger volume of HSR was established. The DLP-WNN model obtains the daily forecast result by weighed summation of the daily output values of the two subnets. Subnet 1 reflects the overall trend of daily passenger volumes in the recent period, and subnet 2 the daily fluctuation of the daily passenger volume to ensure the accuracy of medium-term forecast.FindingsAccording to the example application, in which the DLP-WNN model was used for the medium-term forecast of the daily passenger volumes for 120 days for typical O-D pairs at 4 different distances, the average absolute percentage error is 7%-12%, obviously lower than the results measured by the Back Propagation (BP) neural network, the ELM (extreme learning machine), the ELMAN neural network, the GRNN (generalized regression neural network) and the VMD-GA-BP. The DLP-WNN model was verified to be suitable for the medium-term forecast of the daily passenger volume of HSR.Originality/valueThis study proposed a Double Layer Parallel structure forecast model for medium-term daily passenger volume (about 120 days) of HSR systems by using the date and holiday labels and Wavelet Neural Network. The predict results are important input data for supporting the line planning, scheduling and other decisions in operation and management in HSR systems.

Chaotic Characteristic Analysis of Dynamic Gravity Model with Fractal Structures via an Improved Conical Volume-Delay Function

Road traffic networks are chaotic and highly complex systems. In this paper, we introduce a dynamic gravity model that characterizes the behaviors of the O-D (origin-destination) traffic, such as equilibrium, period-doubling, chaos, and fractal in discrete time. In cases where the original cost function is used, the trip distribution model might degenerate into an all-or-nothing problem without the capacity constraints. To address this shortcoming, we propose substituting the original cost function with an improved conical volume-delay function. This new function retains some of the properties of the original cost function, and its parameters have the same meaning as those in the original function. Our analysis confirms that the double-constrained dynamic gravity model successfully characterizes complex traffic behavior because of the improved conical volume-delay function. Our analysis further shows that the three-parameter bifurcation diagram based on the period characteristics provides deep insight into the actual state of the road traffic networks. Investigating the properties of the model solutions, we further show that the new model is more effective in addressing the all-or-nothing problem.

Ranking Spatial Units with Structural Property and Traffic Distributions for Uncovering Spatial Interaction Patterns in a City

Travel activity data mining is critical to numerous urban applications such as transportation and location‐based services. This article studies the spatial units ranking algorithm for uncovering spatial interaction patterns based on the flow properties of people's travel trajectories. For example, using a taxi origin–destination flow database, a user may want to rank the origin and destination with respect to their functional importance within the urban activity space. In the literature, such an importance concept is usually specified via the frequency function of trip flows. Considering the case that the less frequently visited place in reality may still be an important origin or an important destination, we propose a different method for the ranking of spatial units by introducing the structural property of trip network. The proposed method is inspired from the mutual reinforcing relationship between the trip origins and destinations: important destinations attract travel flows from important origins and at the same time important origins have many flows toward important destinations. Our experimental results show that the proposed method is effective in uncovering spatial interaction patterns of urban activities.

The Market for Economics and Finance PhDs

AbstractThis paper presents new information about the post‐graduation activities of those with a PhD in economics and finance from an Australian university. Approximately 40 per cent have an academic job, while the other 60 per cent work elsewhere or engage in other activities. The analysis includes origin‒destination networks for both the academic and non‐academic markets, the determinants of earnings and measures of overqualification and underemployment. The findings of the paper can provide guidance for those completing or contemplating PhD studies.

Submodularity of optimal sensor placement for traffic networks

The need for monitoring the state of a traffic network versus the costly installation and maintenance of roadside sensors constitutes the tough sensor placement problem in designing transportation networks. Placement problems naturally lie in the category of subset selection problems, which are known to be inherently combinatorial, and therefore, finding their exact solution is intractable for large problems. Due to this intractability, numerous heuristics have been proposed in the literature for approximately solving placement problems for traffic networks. Among these approaches, it has been observed that greedy algorithms normally outperform other heuristics. In this paper, we show the mathematics of why greedy algorithms are appropriate proxies for solving these subset selection problems; similar to placement problems for linear systems, placement problems for traffic networks also normally have a submodular structure. In this work, we analyze the problem of road sensor placement for transportation networks under different information structures available: when no vehicle routing information is available, when vehicles’ routings are known, and when it is necessary to maximize the number of origin–destination (O/D) traffic flows that are monitored with a set of sensors. We show that in all these cases, the placement problem has a submodular monotone structure. It is well known that the submodularity and monotonicity of discrete optimization problems can be leveraged to derive greedy algorithms that approximate the optimal solution. Consequently, our result is of great practical importance since by exploiting the submodularity and monotonicity of a problem, we show that it is possible to use polynomial-time greedy algorithms to approximate the combinatorial optimization problem with guaranteed optimality bounds for large problems, which are intractable to solve otherwise. Our results shed light upon the success of heuristic greedy algorithms that have been developed in some of the literature for solving placement problems at scale. To demonstrate the applicability of submodular optimization for solving placement problems, we first compare the performance of our polynomial-time approximation algorithm with the true optimum in an example traffic network which is small enough for finding the exact optimal solution with enumerating all possible subsets. Then, we investigate and validate our submodular approach in a case study involving a large-scale traffic network in Berkeley, California, where finding the exact optimal solution is intractable. Submodularity of the placement problem in these scenarios provides a powerful computational tool which can be further extended to other placement problem formulations that can become a reference for solving similar problems in the transportation literature.

Origin-Destination Traffic Survey—Case Study: Data Analyse for Bacau Municipality

In order to develop a transport model for the municipality of Bacau, it was necessary to collect data on the current mobility characteristics of people and goods. Traffic data were collected by means of using an origin-destination (O-D) survey. This survey was carried out in the form of a manual traffic census arranged in stations at six points on the entrance sectors of the national roads in the city of Bacau. At the level of the municipality of Bacau, data from a sample of 3040 drivers were used for the origin-destination surveys. The respondents answered 11 questions. The article presents the results obtained from the six control points for seven questions that were considered relevant. The data obtained were initially used to identify, from a percentage and quantitative point of view, the type of traffic specific to the municipality of Bacau. The analysis of the data shows that the majority of vehicles, 78.9%, originate from Bacau County and that 87.2% of those interviewed have Bacau County as their destination. Most vehicles passing through the checkpoints were within the time intervals 7:30–8:30, i.e., 15.55%, and 16:30–17:30, i.e., 16.18%. The highest proportion of registered vehicles were from Romania, 97.99%. Additionally, 40.75% of the respondents were travelling for business purposes, and approximately the same share was found for the number of people in the vehicle; i.e., single occupant vehicles comprised 58.35% of the total vehicles surveyed. Passenger cars accounted for 67.66% of the vehicles. By using the OriginLab software, the aim was to create parallel graphs for each control point in order to identify certain correlations between the data obtained from the questionnaire. At the end of the article, a hierarchical cluster statistical analysis was carried out by using OriginLab, which identified a series of correlations between the analysed parameters.

ODformer: Spatial–temporal transformers for long sequence Origin–Destination matrix forecasting against cross application scenario

Origin–Destination (OD) matrices record directional flow data between pairs of OD regions. The intricate spatiotemporal dependency in the matrices makes the OD matrix forecasting (ODMF) problem not only intractable but also non-trivial. However, most of the related methods are designed for very short sequence time series forecasting in specific application scenarios, which cannot meet the requirements of the variation in scenarios and forecasting length of practical applications. To address these issues, we propose a Transformer-like model named ODformer, with two salient characteristics: (i) the novel OD Attention mechanism, which captures special spatial dependencies between OD pairs of the same origin (destination), greatly improves the ability of the model to predict cross application scenarios after combining with 2D-GCN that captures spatial dependencies between OD regions. (ii) a PeriodSparse Self-attention that effectively forecasts long sequence OD matrix series while adapting to the periodic differences in different scenarios. Generous experiments in three application backgrounds (i.e., transportation traffic, IP backbone network traffic, crowd flow) show that our method outperforms the state-of-the-art methods.

Prediction of estimated time of arrival for multi-airport systems via “Bubble” mechanism

Predicting Estimated Time of Arrival (ETA) for a Multi-Airport System (MAS) is much more challenging than for a single airport system because of complex air route structure, dense air traffic volume and vagaries of traffic conditions in an MAS. In this work, we propose a novel “Bubble” mechanism to accurately predict medium-term ETA for a Multi-Airport System (MAS), in which the prediction of travel time of an origin–destination (OD) pair is decomposed into two stages, termed as out-MAS and in-MAS stages. For the out-MAS stage, Auto-Regressive Integrated Moving Average (ARIMA) is used to predict the travel time of a flight to reach the MAS boundary. For the in-MAS stage, we construct new spatio-temporal features based on clustering analysis of trajectory patterns facilitated by a novel data-driven hybrid polar sampling method. A sequence-to-sequence prediction model, Multi-variate Stacked Fully connected Bidirectional Long–Short Term Memory, is further developed to achieve multi-step-ahead predictions of in-MAS travel time for each trajectory pattern using the spatio-temporal features as input. Finally, the medium-term ETA prediction for an MAS is achieved by integrating the out-MAS and in-MAS prediction with the help of trajectory pattern prediction via random forest. A case study of predicting medium-term ETA for a typical MAS in China, Guangdong–Hong Kong–Macao Greater Bay Area, is conducted to demonstrate the usage and promising performance of the proposed method in comparison to several commonly used end-to-end learning methods.

RCMVis: A Visual Analytics System for Route Choice Modeling.

We present RCMVis, a visual analytics system to support interactive Route Choice Modeling analysis. It aims to model which characteristics of routes, such as distance and the number of traffic lights, affect travelers' route choice behaviors and how much they affect the choice during their trips. Through close collaboration with domain experts, we designed a visual analytics framework for Route Choice Modeling. The framework supports three interactive analysis stages: exploration, modeling, and reasoning. In the exploration stage, we help analysts interactively explore trip data from multiple origin-destination (OD) pairs and choose a subset of data they want to focus on. To this end, we provide coordinated multiple OD views with different foci that allow analysts to inspect, rank, and compare OD pairs in terms of their multidimensional attributes. In the modeling stage, we integrate a k-medoids clustering method and a path-size logit model into our system to enable analysts to model route choice behaviors from trips with support for feature selection, hyperparameter tuning, and model comparison. Finally, in the reasoning stage, we help analysts rationalize and refine the model by selectively inspecting the trips that strongly support the modeling result. For evaluation, we conducted a case study and interviews with domain experts. The domain experts discovered unexpected insights from numerous modeling results, allowing them to explore the hyperparameter space more effectively to gain better results. In addition, they gained OD- and road-level insights into which data mainly supported the modeling result, enabling further discussion of the model.

Origin-destination inference in public transportation systems: A comprehensive review

Origin-destination (OD) modeling facilitates effective demand-responsive public transportation planning in order to meet emergent needs. Given recent advances in transit information and personal communications technology, transit OD estimation methods have evolved from relying on limited survey sources to automated big data sources. Innovative modeling approaches have also been developed over several decades to estimate trip ODs, not only for single routes, but also for full networks, including transfers. In this paper, we synthesize a review of the state of the art in research and practice, along with descriptions of key data types and methodological approaches, indicating how they interact. We also discuss current research gaps and opportunities for further innovation. This review provides a comprehensive resource that should facilitate the application of these methods to various transit systems, thus enabling planners and policymakers to gain insights from new and improved model estimates in various transit systems.

Realistic traffic model for urban environments based on induction loop data

As we gradually move towards smarter cities, having greater control of the traffic in the city becomes of utmost importance. In addition, to efficiently manage such traffic, it is critical to be able to predict the impact of different traffic policies, and potential changes to the city road systems structure. To this end, accurate traffic simulation models must be derived that can help in this task. This paper presents a tool that aims to improve the representativeness of traffic simulations by generating realistic Origin-Destination (OD) traffic matrices. In particular, we focus on cities whose source of traffic information are the induction loop detectors deployed through the different streets and avenues of the city. By comparing against the widely used DFROUTER tool, part of the SUMO open-source traffic simulation package, we show how we are able to improve the traffic model accuracy significantly. Specifically, we achieved more realistic route lengths and a better distribution of traffic sources and destinations.

Modeling and Solving a Linear Integer Problem (PLNE) for the Optimal Localization of a Hub Air Transport in the WAEMU Zone

In this article, we propose a linear integer model for the optimal location of a hub for air traffic in the WAEMU zone. A hub represents for an airline a base where an essential part of its activities is concentrated. Its location must therefore be judiciously determined. The hub location problem is one of the new and promising areas of research in the field of location theory. In order to satisfy a demand, the location of the hub involves the movement of people, goods between origin destination pairs required. Hubs are applied to reduce the number of transport links between the origin and destination airports. The proposed model minimizes the distances and takes into account the flow of passengers registered in the different airports and minimizes the total cost of the transfer via the hub airport. The simulations were made with the programming language Python.

A Unique Metro Choice Behaviour of Suburban Rail Passengers in India

This study aims to analyse the inter-rail modal shift behaviour of suburban rail passengers to examine ridership for the proposed metro extension corridor in Chennai, India. This investigation was conducted in 2019 as part of a feasibility study for the extension of the metro line spanning between Chennai Airport and Kilambakkam, a southern suburb of Chennai. The same origin–destination pair is also served by the suburban rail system. It is an extension of the operating line from Washermenpet to the airport of the Phase I metro project. For this inter-rail competition study, a sample of 272 suburban rail passengers covering work, education and other trip purposes were interviewed using a stated preference questionnaire. Six stated scenarios were considered for analyses which included travel time saving by using the metro along with the fare difference between metro and bus. The study revealed that suburban rail passengers were less concerned about travel time saving and gave priority to fare difference irrespective of trip purpose. This shows the unique metro choice behaviour of suburban rail travellers in the Indian context.

Nash Equilibrium and Stackelberg Approach for Traffic Flow Optimization in Road Transportation Networks—A Case Study of Warsaw

The article discusses the issue of modelling traffic flows and the transport network. Faced with an increase in the number of vehicles in road networks, the problem of congestion and the need to optimise traffic and adapt the transport infrastructure to changing demand are growing, especially in large cities. With this in mind, the authors of this publication developed a model of the road network in the north-eastern part of the Warsaw agglomeration based on the proposed algorithm. Two methods were used to optimise the distribution of traffic flows: the Nash equilibrium and the Stackelberg approach. The Nash equilibrium assumes the aim of achieving equal average times on all roads for each origin–destination (O-D) pair. This describes the state pursued by a decentralised system guided by the individual benefits of the traffic users. On the contrary, the Stackelberg approach aims to achieve optimal travel times for the entire system. The study was carried out for three scenarios that differed in the assumed traffic demand on the road network. The basic scenario assumed the average hourly traffic demand during the morning peak hour based on traffic measurements. On the other hand, the two alternative scenarios were developed as a 10% variation in traffic volumes from the baseline scenario. On the basis of the results, it was concluded that an increase in traffic volumes for all O-D pairs could result in a decrease in traffic volumes on some links of the road network. This means that the transport network is a complex system and any change in parameters can cause significant and difficult to predict changes. Therefore, the proposed approach is useful in terms of traffic forecasting for road networks under conditions of changing traffic flow volumes. Additionally, the total travel time for the entire system differed for each scenario by a percentage difference of 0.67–1.07% between the optimal solution according to the Nash equilibrium and the Stackelberg approach.