Origin-destination Information Research Articles

Short‐term origin‐destination demand forecasting in bus rapid transit based on dual attentive multi‐scale convolutional network

AbstractThe short‐term origin‐destination (OD) forecast in bus rapid transit (BRT) can provide future OD demand, which means that transport plan personnel can take measures in advance, such as adjusting the departure schedule, to avoid congestion. This emphasizes the importance of accurate and stable OD forecasting in real‐time. Deep learning methods have been widely applied due to their superior performance in capturing dynamic spatiotemporal and nonlinear features. However, current methods based on deep learning and data‐driven cannot simultaneously address the problems of data availability and sparsity, and their ability to capture dynamic spatiotemporal patterns is insufficient, which cannot provide satisfactory accuracy and stability. To address these problems, this paper proposes a method for forecasting the OD demand in BRT based on dual attention multi‐scale convolutional network (DAMSCN). Considering the three temporal properties of OD, that is, closeness, period and trend dependencies. DAMSCN uses multi‐scale convolution (MSC), dual attention mechanism (DAt) and stacked convolutional long short‐term memory networks (ConvLSTM) to model the three kinds of properties. The MSC module converts sparse OD information into dense useful features to solve the problem of data sparsity. The DAt and ConvLSTM module model the dependencies relationship in the spatial and temporal dimensions, which can improve the spatiotemporal features extraction ability. DAMSCN also combine inflow information to enhance data availability. The three properties are dynamically aggregated and further integrated with external influences (e.g. weather conditions etc.) to obtain the final forecasting result. The accuracy and stability are evaluated on the Xiamen BRT (XMBRT) and Shanghai Metro datasets (SHMetro). The experimental results show that the DAMSCN is superior to other method. Compared to the best baseline, on the XMBRT, MAE decreased by 8.60%, and RMSE decreased by 4.81%. On the SHMetro, MAE decreased by 7.22%, and RMSE decreased by 8.96%.

Decarbonization in Mexico by extending the charging stations network for electric vehicles

Climate change is an important phenomenon causing significant issues and concerns worldwide. As a consequence, many countries are making efforts to meet the goals proposed in the Kyoto Protocol and the Paris Agreement to reduce CO2 emissions. This paper explores the impact of electric vehicles on the decarbonization process in Mexico. Some scenarios are studied to propose an extension of the current charging stations network. These scenarios are modeled using a mathematical model that optimizes the location of such stations considering the origin-destination information available, a particular type of station, and different vehicle types and covered distances. The decarbonization impact is calculated based on the savings in CO2 emissions for the profile of electric energy generation in Mexico. The results for each scenario show that relevant coverage in the country can be achieved with a mild increase in the number of stations in strategic locations and with a benefit in the decarbonization effect.

Uncovering Equity and Travelers’ Behavior on the Expressway: A Case Study of Shandong, China

Understanding equity and travelers’ behavior plays a key role in creating suitable strategies to promote the development of the expressway. Especially, finding clusters of expressway users could help managers provide targeted policies in order to enhance service quality. However, it is challenging to identify expressway travel behaviors, such as traffic flow distribution and users’ classification. Electronic toll collection (ETC) has been widely applied to improve expressway management, because it can record the origin–destination information of users. This paper proposes a framework to analyze the equity and travel behavior of expressway users with a large amount of ETC data. In the first stage, the Gini coefficient is adopted to analyze expressway equity. In the second stage, 12 kinds of indicators are extracted, including number of trips, car type, mean distance, etc. In the third stage, kmeans algorithm is adopted to cluster the users, based on the introduced indicators. Finally, we analyze the traffic flow distribution of each group by constructing a traffic flow network. The results show that the Gini coefficient is 0.4193, which demonstrates evident inequity in the expressway service. Moreover, statistical analysis shows that expressway flow is complicated and 70.77% of travelers do not make repeat trips. It is demonstrated that expressway users can be divided into six groups, and the flow networks of cluster 2 and cluster 3 are connected more closely and evenly than other clusters are.

Urban Road Network Partitioning Based on Bi-Modal Traffic Flows With Multiobjective Optimization

The recent extension of a macroscopic fundamental diagram (MFD) into a bi-modal MFD (or 3D-MFD) provides the relationship among the total network circulating flows and the accumulations of private vehicles and public buses. 3D-MFD reveals the contribution of large occupancy vehicles such as buses in improving urban transportation efficiency. A lot of bi-modal traffic management techniques are introduced based on 3D-MFD to improve the urban traffic efficiency without using detailed origin-destination (OD) information. However, similar to MFD, 3D-MFD is also highly affected by the heterogeneity of a road network. In order to form 3D-MFDs with low scatter to be utilized for further bi-modal traffic management, this paper proposes a partition method to cluster road links into several homogeneous regions for a bi-modal urban network. It is comprised of three layers named as initial partition, merging, and boundary adjusting. At the initial partition layer, Seeded Region Growing (SRG) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are integrated to obtain a number of subregions. A modified Genetic Algorithm (GA) is developed to merge the subregions into larger regions at the merging layer. Then, boundary adjusting is performed by changing the region to which a boundary is clustered to optimize the result. Multi-sensor data collected from Shenzhen in China are utilized to verify the effectiveness of the proposed partition method.

An Origin-Destination Demands-Based Multipath-Band Approach to Time-Varying Arterial Coordination

With the development of intelligent transportation technology, more and more traffic information can be obtained to enhance arterial coordination efficiency. A time-varying arterial coordination control program is proposed in this paper. By extracting Origin-Destination (OD) information from Automatic number plate recognition (ANPR) data, an OD-based Multipath-Band method (MP-BAND) is developed to achieve the function of the program. The proposed method breaks vehicle routing at intersections, and treats one link as an analysis unit. The method can automatically select link paths for coordination, and multiple paths can be synchronized simultaneously according to the actual traffic condition. The overlap bandwidth is introduced to capture the connectivity between paths of adjacent intersections. The PM-BAND is formulated as a mixed-integer linear program, which can be solved by the standard branch-and-bound technique. Numerical tests are conducted to evaluate the performance of MP-BAND under different traffic conditions. The results have demonstrated that MP-BAND outperformed MULTIBAND and AM-BAND in various aspects. MP-BAND can improve network performance significantly almost in all scenarios. Moreover, the intersection efficiency was improved significantly, especially the efficiency of left-turning vehicles. Also, MP-BAND can well recognize major routes and improve traffic efficiency. Compared with Synchro, MP-BAND also had superiority when flow rate is not too high. Thus, MP-BAND has a much wider applicability, and can be treated as an optimization core to achieve time-varying arterial coordination.

Estimating Spatiotemporal Contacts Between Individuals in Underground Shopping Streets Based on Multi-Agent Simulation

Coronavirus disease 2019 (COVID-19) has exposed the public safety issues. Obtaining inter-individual contact and transmission in the underground spaces is an important issue for simulating and mitigating the spread of the pandemic. Taking the underground shopping streets as an example, this study aimed to verify commercial facilities’ influence on the spatiotemporal distribution of inter-individual contact in the underground space. Based on actual surveillance data, machine learning techniques are adopted to obtain utilizers’ dynamics in underground pedestrian system and shops. Firstly, an entropy maximization approach is adopted to estimate pedestrians’ origin-destination (OD) information. Commercial utilization behaviors at different shops are modeled based on utilizers’ entering frequency and staying duration, which are obtained by re-identifying individuals’ disappearances and appearances at storefronts. Based on observed results, a simulation method is proposed to estimate utilizers’ spatiotemporal contact by recreating their space-time paths in the underground system. Inter-individual contact events and exposure duration are obtained in view of their space-time vectors in passages and shops. A social contact network is established to describe the contact relations between all individuals in the whole system. The exposure duration and weighted clustering coefficients were defined as indicators to measure the contact degree of individual and the social contact network. The simulation results show that the individual and contact graph indicators are similar across time, while the spatial distribution of inter-individual contact within shops and passages are time-varying. Through simulation experiments, the study verified the effects of self-protection and commercial type adjustment measures.

Effects and feasibility of shared mobility with shared autonomous vehicles: An investigation based on data-driven modeling approach

Shared mobility is a promising travel mode in the era of autonomous driving. Travelers may no longer own a vehicle, but use shared autonomous vehicle (SAV) services. This study investigates the effects and feasibility of SAV-based shared mobility, which includes ride-sharing and car-sharing strategies, by using a data-driven modeling approach. Ride-sharing indicates that two trips with similar origin–destination information can be combined into a new one, whereas car-sharing indicates that trips can be fulfilled by a single vehicle consecutively. On the basis of license plate recognition data of Langfang, China, this study extracts the urban-scale vehicle travel demand information. Models for ride-sharing and car-sharing are formulated to generate SAV assignment strategies for fulfilling travel demands. This study reveals the prospects and potential problems of SAV-supported shared mobility at different development stages by setting a variety of scenarios with different participation levels of ride-sharing and car-sharing. The minimum fleet size to fulfil the vehicle travel demand in the road network and the total vehicle stock in the urban area are compared under different scenarios, and the effects of shared mobility on vehicle kilometers traveled (VKT) and parking demand are evaluated. This study also reveals the impacts of SAVs in a practical scenario, which is constructed based on an online survey. Results show that ride-sharing and car-sharing with high participation will lead to considerable benefits, i.e., reductions in fleet size, vehicle stock, and parking demand. Under the shared mobility scenario with 100% ride-sharing and car-sharing participation levels, one SAV can potentially replace 3.80 private conventional vehicles in the road network. However, ride-sharing and car-sharing exhibit opposite effects on VKT. Car-sharing alone increases VKT whereas car-sharing and ride-sharing together have the potential to decrease VKT. This study provides insights for understanding the development of shared mobility and facilitating the efficient utilization of SAVs.

A Python library for exploratory data analysis on twitter data based on tokens and aggregated origin–destination information

Twitter is perhaps the social media more amenable for research. It requires only a few steps to obtain information, and there are plenty of libraries that can help in this regard. Nonetheless, knowing whether a particular event is expressed on Twitter is a challenging task that requires a considerable collection of tweets. This proposal aims to facilitate, to a researcher interested, the process of mining events on Twitter by opening a collection of processed information taken from Twitter since December 2015. The events could be related to natural disasters, health issues, and people’s mobility, among other studies that can be pursued with the library proposed. Different applications are presented in this contribution to illustrate the library’s capabilities: an exploratory analysis of the topics discovered in tweets, a study on similarity among dialects of the Spanish language, and a mobility report on different countries. In summary, the Python library presented is applied to different domains and retrieves a plethora of information in terms of frequencies by day of words and bi-grams of words for Arabic, English, Spanish, and Russian languages. As well as mobility information related to the number of travels among locations for more than 200 countries or territories.

Rail transit OD‐matrix completion via manifold regularized tensor factorisation

Urban rail transit has become an indispensable mode in major cities worldwide regarding the advantages of large capacity, high speed, punctuality, and environmental protection. Origin-destination (OD) matrix data is crucial to the organisation of rail train operation and management. Nevertheless, rail transit OD matrices are inevitably suffered from data loss problems due to the data transmission and acquisition failures. Tensor completion is a state-of-the-art method for missing data imputation. In this paper, a novel tensor completion method for OD- matrix completion is proposed. To this end, an OD-matrix tensor is established to represent OD information, and the similarity matrix of OD-matrix tensor for each dimension is extracted as a piece of auxiliary information expressing underlying multi-mode relationships of OD data. Finally, a manifold regularised tensor factorisation is applied to impute the missing OD data, in which the Graph Laplacians inferred from similarity weight matrices are used as regularisation priors on factorisation factors. The proposed model is applied to a case study of the metro line in Xi'an, China. The experimental results indicate that the proposed method outperforms baselines. It can accurately impute missing data within the OD matrices and work well even when the missing ratio is up to 80%.

TaxiInt: Predicting the Taxi Flow at Urban Traffic Hotspots Using Graph Convolutional Networks and the Trajectory Data

Taxi flow is an important part of the urban intelligent transportation system. The accurate prediction of taxi flow provides an attractive way to find the potential traffic hotspots in the city, which helps to avoid serious traffic congestions by taking effective measures in advance. The current prediction of taxi flow and its impact on urban transportation are closely related to the passenger origin-destination (OD) information. However, high-quality OD information is not always available. To address this problem, a prediction model, named as TaxiInt, is proposed in this study. Different from other density-clustering-based approaches, neural network, or OD information based models, TaxiInt predicted the taxi flow using the trajectory data of taxis. The spatial features and temporal features of each road were extracted using a graph convolutional network, which was trained with the road network information and the trajectory data. The experiments carried on a real taxi dataset showed the validity of our model. It can predict the taxi flow at a given urban intersection with high accuracy.

Monitoring Public Transit Ridership Flow by Passively Sensing Wi-Fi and Bluetooth Mobile Devices

Real-time public transit ridership flow and origin-destination (O-D) information is essential for improving transit service quality and optimizing transit networks in smart cities. The effectiveness and accuracy of the traditional survey-based methods and smart card data-driven methods for O-D information inference have multiple disadvantages in terms of biased results, high latency, insufficient sample size, and the high cost of time and energy. By considering the ubiquity of smart mobile devices in the world, monitoring public transit ridership flow can be accomplished by passively sensing Wi-Fi and Bluetooth (BT) mobile devices of passengers. This study proposed a system for monitoring real-time public transit passenger ridership flow and O-D information based on customized Wi-Fi and BT sensing device. By combining the consideration of the assumed overlapping feature spaces of passenger and nonpassenger media access control address data, a three-step data-driven algorithm framework for estimating transit ridership flow and O-D information is proposed. The observed ridership flow is used as the ground truth for evaluating the performance of the proposed algorithm. According to the evaluation results, the proposed algorithm outperformed all selected baseline models and the existing filtering methods. The findings of this study can help to provide real time and precise transit ridership flow and O-D information for supporting transit vehicle management and the quality of service enhancement.

Metro OD Matching and Statistical Research Based on AFC Data

With the development of the smart city system, it is more and more import to improve China’s rail transit system and create a convenient traffic environment for passengers. This article will use the AFC data of Chongqing Regional Rail Transit for two months for OD(origin-destination) matching and passenger flow statistics. The main work includes AFC(Automatic Fare Collection) data cleaning, OD information matching, data conversion, passenger flow statistics and data visualization under the big data environment. At the same time, in the construction of the data platform, three schemes are currently adopted. One is to adopt the traditional method of java-Oracle when the amount of data is small. The second is to use the classic method of Hadoop-Hive when the amount is large. The third is to use parallel computing Maxcompute for large amounts of data to improve data calculation speed. Through the experimental comparison of the three schemes, the exact matching of the site, line, time and passenger flow statistics, in terms of speed, the parallel computing method is used to calculate the fastest data. This paper based on the AFC data of metro OD matching and statistical research can help the transformation of urban traffic structure from extensive to fine and provide support for urban transportation planning and decision-making.

A comparison in travel patterns and determinants of user demand between docked and dockless bike-sharing systems using multi-sourced data

The co-existence of traditional docked bike-sharing and emerging dockless systems presents new opportunities for sustainable transportation in cities all over the world, both serving door to door trips and accessing/egressing to/from public transport systems. However, most of the previous studies have separately examined the travel patterns of docked and dockless bike-sharing schemes, whereas the difference in travel patterns and the determinants of user demand for both systems have not been fully understood. To fill this gap, this study firstly compares the travel characteristics, including travel distance, travel time, usage frequency and spatio-temporal travel patterns by exploring the smart card data from a docked bike-sharing scheme and trip origin–destination (OD) data from a dockless bike-sharing scheme in the city of Nanjing, China over the same spatio-temporal dimension. Next, this study examines the influence of the bike-sharing fleets, socio-demographic factors and land use factors on user demand of both bike-sharing systems using multi-sourced data (e.g., trip OD information, smart card, survey, land use information, and housing prices data). To this end, geographically and temporally weighted regression (GTWR) models are built to examine the determinants of user demand over space and time. Comparative analysis shows that dockless bike-sharing systems have a shorter average travel distance and travel time, but a higher use frequency and hourly usage volume compared to docked bike-sharing systems. Trips of docked and dockless bike-sharing on workdays are more frequent than those on weekends, especially during the morning and evening rush hours. Significant differences in the spatial distribution between docked and dockless bike-sharing systems are observed in different city areas. The results of the GTWR models reveal that hourly docked bike-sharing trips and dockless bike-share trips influence each other throughout the week. The density of Entertainment points of interest (POIs) is positively correlated with the usage of dockless bike-sharing, but negatively correlated with docked bike-sharing usage. On the contrary, the proportion of the elderly has a positive association with the usage of docked bike-sharing, but a negative association with the usage of dockless bike-sharing. Finally, policy implications and suggestions are proposed to improve the performance of docked and dockless bike-sharing systems, such as increasing the flexibility of docked bike-sharing, designing and promoting mobile applications (APP) for docked bike-sharing, improving the quality of dockless shared bikes, and implementing dynamic time-based pricing strategies for dockless bike-sharing.

Inferring origin-Destination demand and user preferences in a multi-modal travel environment using automated fare collection data

Eliciting individual travelers’ Origin-Destination (OD) information is critical for enabling public transit system policy-makers and operators to serve travelers in a calculated way. Accurate estimation of route choice model parameters is also important, in that it can help assess or predict the service levels that such a system can be expected to achieve. The knowledge of both the OD links and route choice logic is especially in demand for emerging mobility services, where providers work to accommodate individualized services and also offer incentives to travelers for specific trips. We show that all this information can be distilled from a particular type of data – the Automated Fare Collection (AFC) system data – in a fast, low-cost way. This paper presents a two-step methodological framework to identify individual travelers’ true ODs (beyond stop-level ODs), as well as infer their travel preferences. The key to our work is the ability to identify and process the observations of travelers’ routing choices between the same ODs under different travel environment conditions. A presented specially-crafted case study validates the proposed method in application with a real-world AFC data of Seoul, Korea, confirming the method’s high inferential ability, under a basic route choice model.

Exploring the Weekly Travel Patterns of Private Vehicles Using Automatic Vehicle Identification Data: A Case Study of Wuhan, China

Automatic vehicle identification (AVI) systems collect 24 h vehicle travel data for the efficient management of traffic flows. The automatic vehicle identification data collected by an overhead traffic monitoring system provides a means for understanding urban traffic flows and human mobility. This article explores the weekly travel patterns of private vehicles based on AVI data in Wuhan, a megacity in Central China. We extracted origin–destination information and applied the K-Means clustering algorithm to classify spatial traffic hot spots by camera locations. Subsequently, the Latent Dirichlet Allocation algorithm was used to mine the temporal travel patterns of individual vehicles. The cluster results are summarized in nine travel probability matrixes. The effectiveness of this approach is illustrated by a case study using a large set of AVI data collected from 19 to 24 November 2018, in Wuhan, China. The results revealed six variations of the travel demand on weekdays and weekends—the commuting behaviors of private drivers triggered a tidal change in traffic flows. This study also exposed nine weekly travel patterns for private cars, reflecting temporal similarities of human mobility patterns. We identified four types of commuters. These results can help city managers understand daily changes in urban travel demands.

Validating traffic models using large-scale Automatic Number Plate Recognition (ANPR) data

The development of reliable strategic traffic models relies on comprehensive and accurate data, but traditional survey methods are time-consuming and expensive. Manual surveys often yield small samples that require estimated expansion factors to enable the data to represent the population. Modellers have turned to new data sourced from various electronic devices to improve the reliability of the data. Automatic Number Plate Recognition (ANPR) data is one such data source that can be used to extract travel time, speed and partial origin-destination (OD) information. This study assesses ANPR data in terms of its comprehensiveness and accuracy, and shows how it can be used for the validation of strategic traffic models. Data was obtained from the Gauteng freeway system's Open Road Tolling (ORT) gantries for a period of several months. A new methodology is developed to process traffic model outputs such that they are directly comparable to the partial origin-destination outputs derived from the ANPR data. It is shown that comparing the model distribution against observed ANPR data highlights potential trip distribution issues that are not detected using standard model validation techniques.

Estimation of traffic flow changes using networks in networks approaches

Understanding traffic flow in urban areas has great importance and implications from an economic, social and environmental point of view. For this reason, numerous disciplines are working on this topic. Although complex network theory made their appearance in transportation research through empirical measures, the relationships between dynamic traffic patterns and the underlying transportation network structures have scarcely been investigated so far. In this work, a novel Networks in Networks (NiN) approach is presented to study changes in traffic flows, caused by topological changes in the transportation network. The NiN structure is a special type of multi-layer network in which vertices are networks themselves. This embedded network structure makes it possible to encode multiple pieces of information such as topology, paths, and origin-destination information, within one consistent graph structure. Since each vertex is an independent network in itself, it is possible to implement multiple diffusion processes with different physical meanings. In this way, it is possible to estimate how the travellers’ paths will change and to determine the cascading effect in the network. Using the Sioux Falls benchmark network and a real-world road network in Switzerland, it is shown that NiN models capture both topological and spatial-temporal patterns in a simple representation, resulting in a better traffic flow approximation than single-layer network models.

A continuum model for cities based on the macroscopic fundamental diagram: A semi-Lagrangian solution method

This paper presents a formulation of the reactive dynamic user equilibrium problem in continuum form using a network-level Macroscopic Fundamental Diagram (MFD). Compared to existing continuum models for cities – all based in Hughes’ pedestrian model in 2002 – the proposed formulation (i) is consistent with reservoir-type models of the MFD literature, shedding some light into the connection between these two modeling approaches, (ii) can have destinations continuously distributed on the region, and (iii) can incorporate multi-commodity flows without additional numerical error. The proposed multi-reservoir numerical solution method treats the multi-commodity component of the model in Lagrangian coordinates, which is the natural representation to propagate origin-destination information (and any vehicle-specific characteristic) through the traffic stream. Fluxes between reservoir boundaries are computed in the Eulerian representation, and are used to calculate the speed of vehicles crossing the boundary. Simple examples are included that show the convergence of the model and its agreements with the available analytical solutions. We find that (i) when origins and destinations are uniformly distributed in a region, the distribution of the travel times can be approximated analytically, (ii) the magnitude of the detours from the optimal free-flow route due to congestion increase linearly with the inflow and decreases with the square of the speed, and (iii) the total delay of vehicles in the network converges to the analytical approximation when the size of reservoirs tends to zero.

Development and Testing of a Real-Time WiFi-Bluetooth System for Pedestrian Network Monitoring, Classification, and Data Extrapolation

A real-time pedestrian monitoring system provides information about traffic flow, speeds, travel times, and time spent in areas or transportation facilities of interest. This is useful in travel information systems and crowd management strategies, as well as in planning and emergencies in public spaces, such as airports, parks, malls, and university campuses. While there are technologies that can obtain count data for non-motorized transportation at specific locations, most technologies cannot provide origin-destination information, trip paths, travel times, or time spent. To overcome these shortcomings, some studies have explored the use of Bluetooth (BT) sensors to capture the unique media access control (MAC) addresses of mobile devices carried by pedestrians. However, this collection method may suffer from low-detection rates. As an alternative, collecting MAC data from WiFi signals has emerged. The objective of this paper is three-fold: 1) develop and evaluate the performance of an integrated WiFi-BT system to monitor pedestrian-cyclists activity traffic; 2) develop and validate a classification method for differentiating pedestrians from bicycles; and 3) propose a simple extrapolation method that combines counts and MAC data. Among other results, relatively high detection rates were obtained for the developed WiFi system in comparison with BT sensors. In addition, high correlation between estimated and ground truth speeds and low classification errors are observed. Finally, the extrapolated WiFi counts and ground truth counts were found to be highly correlated. These results demonstrate the feasibility of the proposed system and methods to estimate travel times (speeds), to classify bicycle-pedestrian WiFi signals, and to extrapolate pedestrian MAC counts.

Impact of origin-destination information in epidemic spreading

The networked structure of contacts shapes the spreading of epidemic processes. Recent advances on network theory have improved our understanding of the epidemic processes at large scale. The relevance of several considerations still needs to be evaluated in the study of epidemic spreading. One of them is that of accounting for the influence of origin and destination patterns in the flow of the carriers of an epidemic. Here we compute origin-destination patterns compatible with empirical data of coarse grained flows in the air transportation network. We study the incidence of epidemic processes in a metapopulation approach considering different alternatives to the flows prior knowledge. The data-driven scenario where the estimation of origin and destination flows is considered turns out to be relevant to assess the impact of the epidemics at a microscopic level (in our scenario, which populations are infected). However, this information is irrelevant to assess its macroscopic incidence (fraction of infected populations). These results are of interest to implement even better computational platforms to forecast epidemic incidence.