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Study of large rail traffic databases presents formidable challenges for transport system specialists, more particularly while keeping both space and time factors together with the possibility of showing influencing factors related to the users and the transport network environment. To perform such a study, a bibliographic analysis in both statistics and transport revealed that geometrical methods for feature extraction and dimension reduction can be seen as suitable. Since there are several methods/options with, in principle, required input data, this article aims at comparing Principal Component Analysis (PCA) and Correspondence Analysis (CA) for traffic frequency data, both methods being actually used with such data. The procedure stands as follows. First a grand matrix is built where the rows correspond to time windows and the columns to all the possible origin-destination links. Then this large frequency matrix is studied using PCA and CA. The next part of the procedure consists in studying the effects of influencing factors with the possibility of keeping the quantitative scales with PCA or using fuzzy segmentation with CA, the corresponding data being considered as supplementary column points. The procedure is applied on a rail transport network including 10 stations (one corresponding to the airport) and one-hour time windows for 4 months, the available influencing factors being the temperature, rain level and gas price. The comparative analysis shows that CA graphical outputs are more complicated than PCA ones, but reveal more specific results, e.g. the network user behavior related to the airport, while PCA mainly opposes link clusters with low vs. high frequencies. Fuzzy windowing performed using actual and simulated data reduces the loss of information when averaging, e.g. over time, and can show non-linear relational phenomena. The possibility of displaying new traffic data in real time is also considered. [ABSTRACT FROM AUTHOR]

Bus rapid transit systems (BRT) have been an indispensable public transportation pillar, especially in densely populated regions. Accurate insight into the BRT network's utilization is vital in BRT resource allocation planning contexts. This research focuses on how operators can utilize passengers' smart card data to develop origin-destination (OD) matrix-based business analytics. This research proposes a hybrid approach combining trip chaining, direct pairing, mode estimation methods, and visual analytics development. The novel approach is robust in handling incomplete smart card data transactions to generate origin-destination matrices and corresponding visual analytics as decision support systems for the BRT operators. As a case study, we applied and validated the proposed analytics to more than 20.6 million smart card transactions in one of the largest global BRT systems in Jakarta, Indonesia. [ABSTRACT FROM AUTHOR]

This study addresses the urgent need for sustainable urban mobility through a comprehensive analysis of decarbonization potential in the metropolitan area of Valencia, Spain. The research is motivated by the imperative to mitigate climate change amidst high traffic and congestion levels. Utilizing the Origin–Destination matrix from the Valencian community's mobility plan, the study prioritizes displacements for substitution by sustainable alternatives. Through a detailed case study analysis, critical areas for decarbonization are identified, and practical strategies to reduce carbon emissions are proposed. The methodology encompasses a thorough literature review on decarbonization measures, examination of existing plans, and analysis of sustainable trips with high spatial resolution using geographic information systems (GISs). The study identifies trips with significant decarbonization potential and assesses the current public transportation network. Spatial analyses illustrate demand patterns for sustainable transport options, emphasizing the need for tailored strategies. Key findings highlight the role of private and commercial transport in CO2 emissions, advocating for targeted interventions to enhance active transport infrastructure, promote carpooling, adopt low-emission vehicles, and improve public transport accessibility and efficiency. [ABSTRACT FROM AUTHOR]

A key challenge in agent‐based mobility simulations is the synthesis of individual agent socioeconomic profiles. Such profiles include locations of agent activities, which dictate the quality of the simulated travel patterns. These locations are typically represented in origin‐destination matrices that are sampled using coarse travel surveys. This is because fine‐grained trip profiles are scarce and fragmented due to privacy and cost reasons. The discrepancy between data and sampling resolutions renders agent traits nonidentifiable due to the combinatorial space of data‐consistent individual attributes. This problem is pertinent to any agent‐based inference setting where the latent state is discrete. Existing approaches have used continuous relaxations of the underlying location assignments and subsequent ad hoc discretisation thereof. We propose a framework to efficiently navigate this space offering improved reconstruction and coverage as well as linear‐time sampling of the ground truth origin‐destination table. This allows us to avoid factorially growing rejection rates and poor summary statistic consistency inherent in discrete choice modelling. We achieve this by introducing joint sampling schemes for the continuous intensity and discrete table of agent trips, as well as Markov bases that can efficiently traverse this combinatorial space subject to summary statistic constraints. Our framework's benefits are demonstrated in multiple controlled experiments and a large‐scale application to agent work trip reconstruction in Cambridge, UK. [ABSTRACT FROM AUTHOR]

The knowledge of passenger flows between each origin–destination (OD) pair is a main requirement in public transport for service planning, design, operation, and monitoring, and is represented by OD matrices. Although they can be determined by traditional approaches (e.g., surveys, ride-check counts, and/or smartcard-based methods), the availability of new technologies and the proliferation of portable devices triggers an emerging interest in building OD matrices from the apps of bus operators. This research proposes the first framework for the estimation of OD matrices on transit networks by processing smartphone app call detail records (SACDRs). The framework is experimentally tested on a sample of 30 workdays of an Italian bus operator. The results are represented by easy-to-read control dashboards based on maps, which help quantify and visualise the OD matrices in the metropolitan area of Cagliari (Italy). The experimentation shows that the framework can properly estimate the number of trips for both origin and destination w.r.t. OD matrices built from household surveys: the mean absolute error is on average lower than five movements for 90% of the origins and 85% of the destinations. [ABSTRACT FROM AUTHOR]

Although the available traffic data from navigation systems have increased steadily in recent years, it only reflects average travel time and possibly Origin-Destination information as samples, exclusively. However, the number of vehicles participating in the traffic – in other words, the traffic flows being the basic traffic engineering information for strategic planning or even for real-time management – is still missing or only available sporadically due to the limited number of traditional traffic sensors on the network level. To tackle this gap, an efficient calibration process is introduced to exploit the Floating Car Data combined with the classical macroscopic traffic assignment procedure. By optimally scaling the Origin-Destination matrices of the sample fleet, an appropriate model can be approximated to provide traffic flow data beside average speeds. The iterative tuning method is developed using a genetic algorithm to realize a complete macroscopic traffic model. The method has been tested through two different real-world traffic networks, justifying the viability of the proposed method. Overall, the contribution of the study is a practical solution based on commonly available fleet traffic data, suggested for practitioners in traffic planning and management.

This study addresses the urgent need for sustainable urban mobility through a comprehensive analysis of decarbonization potential in the metropolitan area of Valencia, Spain. The research is motivated by the imperative to mitigate climate change amidst high traffic and congestion levels. Utilizing the Origin–Destination matrix from the Valencian community’s mobility plan, the study prioritizes displacements for substitution by sustainable alternatives. Through a detailed case study analysis, critical areas for decarbonization are identified, and practical strategies to reduce carbon emissions are proposed. The methodology encompasses a thorough literature review on decarbonization measures, examination of existing plans, and analysis of sustainable trips with high spatial resolution using geographic information systems (GISs). The study identifies trips with significant decarbonization potential and assesses the current public transportation network. Spatial analyses illustrate demand patterns for sustainable transport options, emphasizing the need for tailored strategies. Key findings highlight the role of private and commercial transport in CO2 emissions, advocating for targeted interventions to enhance active transport infrastructure, promote carpooling, adopt low-emission vehicles, and improve public transport accessibility and efficiency.

The estimation of freight trip distribution is an important issue in freight transport studies, and collecting data on the spatiotemporal aggregated number of freight vehicles without trip information is relatively easy and inexpensive. In this paper, we propose a new model for freight distribution estimation, referred to as “TripCFDM.” Unlike the traditional collective flow diffusion model (CFDM), which solely relies on aggregate data, TripCFDM relies on aggregate data along with a small amount of trip data. CFDM estimates the probability of latent zonal freight trips based on aggregate data corresponding to the number of vehicles in each zone and time step. The use of the aggregate and small amounts of trip data ensures a realistic data acquisition environment. Our model is applied to freight trips in the Tokyo Metropolitan Area using the National Survey of Roads and Streets Traffic Conditions (Road Traffic Census). The results reveal that TripCFDM yields a significant improvement in the estimated probability compared with CFDM, particularly for intrazonal freight trips. However, the estimation results of interzonal trips obtained by CFDM and TripCFDM tend to be similar, suggesting that CFDM alone may be adequate to estimate the probability of interzonal trips with a sufficient degree of accuracy; nevertheless, TripCFDM is a better method for freight trip distribution estimation, including for intrazonal trips. The results of CFDM are not significantly affected by the time steps in the input data, whereas TripCFDM can yield higher accuracies with longer time steps. Regarding the number of zones and zone size, sufficient zone aggregation may be effective in improving the estimation accuracy because a detailed zonal division leads to a small probability of origin–destination trips with extensive computations. Based on this modified CFDM approach, origin and destination matrices for freight transport can be generated without large-scale surveys.

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Estimation (correction) of origin-destination (OD) matrix based on traffic counts data is an inexpensive approach to predicting travel demand in transportation networks. The general formulation of this problem is a bi-level optimization program in which the matrix estimation is solved at the upper level, and the traffic assignment is solved at the lower level. In congested networks, deterministic user equilibrium (UE) assignment is often used at the lower level. Deterministic approaches assume that all users perceive network travel times the same way, which is not the case in reality. In contrast, stochastic methods allow for different user perceptions. This research develops the OD matrix estimation problem (ODMEP) under the stochastic user equilibrium (SUE) constraint. The SUE assignment with the multinomial logit (MNL) route choice model is applied at the lower level. The MNL model is a well-known discrete choice model with a straightforward, closed-form choice probability. Spiess gradient-based approach is used at the upper level, which is efficient in large-scale networks. The Spiess OD estimation models with UE/SUE constraints are implemented on the large-scale Tehran network under different user perception variances represented by the scale parameter (θ) in the MNL formula. Two scenarios are adapted to create the initial OD matrix to compare the results of the two models (ODMEP with UE/SUE assignment). Results show that ODMEP with SUE constraint outperforms ODMEP with UE constraint in producing link volumes close to observed traffic counts. Furthermore, the OD matrix resulting from the SUE-based model is better fitted to the real OD matrix than the UE-based model. However, the two methods' results converge when the scale parameter increases (i.e., variance in users' perceptions of network travel times decreases). In the Tehran network, the SUE-based model reduces the ratio of RMSE of the OD matrix to real demand more than 10 percent (more than 20 percent in some cases) compared to the UE-based model when the scale parameter is less than 0.5.

This paper investigates the opportunities offered by floating car data (FCD) to infer delivering activities. A discrete trip-chain order model (within the random utility theory) for light goods vehicles (laden weight less than 3.5 tons) is hence proposed, which characterizes delivery tours in terms of the number of stops/deliveries performed. Thus, the main goal of the study is to calibrate a discrete choice model to estimate the number of stops/deliveries per tour by using FCD, which can be incorporated in a planning procedure for obtaining a preliminary assessment of parking demand. The data used refer to light goods vehicles operating in the Veneto region. The database contains more than 8000 tours undertaken in 60 working days. Satisfactory results have been obtained in terms of tour estimation and model transferability.

Recent studies have revealed that the dominant traffic pattern of a modern high-rise building is much more complicated than the conventional up-peak model, i.e. incoming traffic only, upon which the popular formula of round trip time (RTT) calculation is based. RTT calculation is still recommended by ISO 8100:32:2020 and CIBSE Guide D: 2020 to precede any computer simulation. Nowadays, the concern is more on the lunch peak with a mixture of incoming, outgoing and interfloor traffic patterns. Moreover, the independent one-passenger arrival model should be replaced by the more practical batch arrival model, in particular during lunch-time peak. This article first summarizes what were previously published on the universal RTT evaluation for mixed traffic conditions, highlights the main arguments, updates and supplements some previously introduced formulae such as that related to coincidental stops, uses more symmetrical and easily manipulated graphics, and, second in particular, brings in the concept of batch arrivals of any desirable probability distribution, where the conventional independent one-passenger arrival up-peak model now becomes a special case. A step-by-step presentation is adopted for convenient application of this universal calculation approach by designers and the detailed verification of the whole process by Monte Carlo simulation is also included. Practical applications: Round trip time calculation is still recommended the first step of traffic design by the industry but it should reasonably include the consideration of mixed traffic patterns, versus a pure incoming (i.e. up-peak) approach in the past. Such universal RTT calculation was described in several articles previously, but with some missing details. Here, the whole process is summarized, updated and presented in a more graphical way for convenient use by designers. Furthermore, the more contemporarily practical batch passenger arrival mode is included in this universal RTT model as an extension to the methodology so that what are discussed in this article include incoming, outgoing, interfloor traffic patterns with batch arrival consideration. It is hoped that this article could provide a comprehensive universal RTT methodology that can eventually be popularly applied globally by designers and consultants. [ABSTRACT FROM AUTHOR]

Shared mobility services, such as bike sharing, have gained immense popularity and emerged as an integral part of sustainable urban mobility solutions. The planning of such systems requires forecasting the potential origin–destination (OD) flows between mobility sites (e.g., bike sharing stations) within the proposed network. Existing methods primarily focus on mobility flows between large regions, and do not generalize well to detailed planning applications due to the high spatial resolution required, with increased uncertainty and data sparsity. This study proposes a zero-inflated negative binomial graph neural network (ZINB-GNN) to generate sparse OD flows while capturing complex spatial dependencies. To reflect sparsity, OD flows are modeled as following ZINB distributions parameterized via feed-forward networks. To capture spatial dependencies, localized graphs are constructed to represent proximity between OD pairs, with spatial features encoded using GNNs. ZINB-GNN is validated through a case study of the bike sharing system in New York City. The results verify its prowess in both prediction accuracy and uncertainty quantification under real-world network expansion scenarios. We also demonstrate its interpretability by revealing important factors affecting OD flows. These findings can directly inform the planning of bike sharing systems, and the methodology may be adapted for other shared mobility systems. • A graph deep learning model is proposed for origin–destination (OD) flow generation. • Zero-inflated negative binomial distributions are adopted to capture data sparsity. • Graph neural networks are used to model spatial dependencies between OD flows. • The model is validated using data from the bike sharing system in New York City. • The results demonstrate improved prediction accuracy and uncertainty quantification. [ABSTRACT FROM AUTHOR]

Abstract Properly extracting patterns of individual mobility with high resolution data sources such as the one extracted from smartphone applications offers important opportunities. Potential opportunities not offered by call detailed records (CDRs), which offer resolutions triangulated from antennas, are route choices, travel modes detection and close encounters. Nowadays, there is not a standard and large scale data set collected over long periods that allows us to characterize these. In this work we thoroughly examine the use of data from smartphone applications, also referred to as location-based services (LBS) data, to extract and understand the vehicular route choice behavior. Taking the Dallas-Fort Worth metroplex as an example, we first extract the vehicular trips with simple rules and reconstruct the origin-destination matrix by coupling the extracted vehicular trips of the active LBS users and the United States census data. We then present a method to derive the commonly used routes by individuals from the LBS traces with varying sample rate intervals. We further inspect the relation between the number of routes and the trip characteristics, including the departure time, trip length and travel time. Specifically, we consider the travel time index and buffer index for the LBS users taking different number of routes. Empirical results demonstrate that during the peak hours, travelers tend to reduce the impact of traffic congestion by taking alternative routes. Overall, the proposed data analysis framework is cost-effective to treat sparse data generated from the use of smartphones to inform routing behavior. The potential in practice is to inform demand management strategies, by targeting individual users while generating large scale estimates of congestion mitigation.

The Origin–Destination (O–D) matrix, is an important information in transportation planning and traffic control. Rapid changes in land use, particularly in developing countries, have been and are on an increase, which makes the estimation and observation of this matrix more significant. The objective of this paper is to observe O–D matrix under two scenarios. In the first scenario, it is assumed that the traffic network is equipped with path-ID sensors. In this situation, the goal is to determine the optimal number and location of these sensors in the network, where by applying collected information through these sensors, the O–D matrix is observed. Because path-ID sensors are not available in many cities, in the second scenario the interview alternative is proposed in order to observe O–D matrix. The interview method has encountered some restrictions. Several mathematical programming models have been developed to overcome these restrictions. To illustrate these proposed methodologies, they are applied in the Nguyen–Dupuis transportation network and the results are analysed. By applying the model on the intercity road network in the Province of Isfahan (Iran), a large network, the efficiency of these proposed models is demonstrated. Finally, some conclusions and final recommendations are included. First published online 10 October 2019

In this paper, the basic concepts of modeling of urban transport systems are analyzed. The known variants of the definition of the concept of the transport system, its main components and the essence of the stages of identification of transport systems (TS) are presented. The example of the Lviv city TS shows the process of modeling the transport network, determining the time characteristics of the modeled transport system and forming a demand matrix for trips of a certain type in a certain segment of users (origin-destination matrix when moving students from their places of compact residence to the nodes of external transport for further implementation of intercity travel). The obtained indicators can be further applied in determining the demand for transportation in transport systems of large cities

Quantifying waterway traffic characteristics based on Automatic Identification System (AIS) data is beneficial to understand and improve traffic conditions. In this paper, a nested loop algorithm is first presented to segment a waterway and to separate vessel trips, where trip directions are categorized to inbound, outbound, and stop status. Next, to increase computational efficiency, the corresponding vectorized algorithm, which rebuilds the AIS data as an array-based structure, is developed, and traffic features such as travel speed, traffic density, traffic flow, trip attraction, trip generation, and origin-destination (O-D) matrices are extracted. Finally, the methodology is applied to the Houston Ship Channel (HSC) as an implementation instance with one month of AIS data, and the traffic features are quantified for different types of vessels with different widths and drafts at different segments. The vectorized algorithm, along with the trip separation, has considerably decreased processing time compared to the loop-based methods. The results of such analysis can be used for short-term operational planning, such as resource allocation and scheduling, or for long-term waterway projects, such as expansions. [ABSTRACT FROM AUTHOR]

Spatiotemporal data, and more specifically origin–destination matrices, are critical inputs to mobility studies for transportation planning and urban management purposes. Traditionally, high-cost and hard-to-update household travel surveys are used to produce large-scale origin–destination flow information of individuals' whereabouts. In this paper, we propose a methodology to estimate origin–destination (O–D) matrices based on passively-collected cellular network signalling data of millions of anonymous mobile phone users in the Rhône-Alpes region, France. Unlike Call Detail Record (CDR) data which rely only on phone usage, signalling data include all network-based records providing higher spatiotemporal granularity. The explored dataset, which consists of time-stamped traces from 2G and 3G cellular networks with users' unique identifier and cell tower locations, is used to first analyse the cell phone activity degree indicators of each user in order to qualify the mobility information involved in these records. These indicators serve as filtering criteria to identify users whose device transactions are sufficiently distributed over the analysed period to allow studying their mobility. Trips are then extracted from the spatiotemporal traces of users for whom the home location could be detected. Trips have been derived based on a minimum stationary time assumption that enables to determine activity (stop) zones for each user. As a large, but still partial, fraction of the population is observed, scaling is required to obtain an O–D matrix for the full population. We propose a method to perform this scaling and we show that signalling data-based O–D matrix carries similar estimations as those that can be obtained via travel surveys. [ABSTRACT FROM AUTHOR]

The paper considers the model of analysis of passenger traffic of the Moscow Metro. The initial information for the analysis is the correspondence matrix describing the number of passengers traveling between pairs of metro stations for a specified time interval. This kind of data is collected on the basis of information from mobile operators. This kind of information is a widely used approach to the presentation of transport data. The article contains a detailed review of transport data analysis models. The paper proposes an approach to the classification of metro stations by type of passenger traffic. The proposed approach compares the entrances and exits of passengers for stations. It is the ratio of these parameters, developed in time, presents the basis for the classification of stations (assigning stations to one of the classes that differ in the nature of the ratio of passenger entry and exit). This allows us, for example, to determine the working and residential areas of the city, as stations where they come to work, and stations from where they go to work. Such an analysis makes it possible to determine the stations that are the receiving point of the external passenger traffic. In some cases, this will be explained by the presence of a number of railway stations and transport hubs, in some cases this will be evidence of the presence of some relatively implicit (unknown) processes in the city (for example, bus lines from the suburbs). Also, changes in the found patterns can serve as evidence of some changes in the life of the city.

Travel demand information in the form of the origin–destination (OD) matrix plays an essential role in studying urban traffic management and network design. The present study takes a novel step toward urban traffic analysis using data mining of processed images of real-time traffic maps as a location-based data model, in which the data were analyzed by software programs such as KNIME and Python workspaces and comparing the results with the conventional traffic assignment results. Thus, we investigated a real-time OD matrix based on the trip-per-vehicle by automatic number-plate recognition (ANPR) cameras for the congestion charge zone (CCZ) of Tehran, Iran. The obtained matrix was assigned to the CCZ transportation network by the convex combination method concerning user equilibrium (UE) condition. The traffic pattern and assignment results were compared to the real traffic data gathered by ANPR, big data analysis and image processing of real-time traffic maps. Considering that the OD based on the trip-vehicle matrix was estimated for vehicle entrances-exits and found to be acceptably accurate compared to real-life conditions, it could be concluded that the UE could not find the practical assignment in 27% of cases in comparison with reality.

Freight vehicle origin-destination (O-D) matrices play a key role within the assessment procedures of city logistics scenarios, and simulation of delivery tours allows such O-D flows to be obtained. Therefore, the paper revisits a modeling framework for simulating delivery tours using an aggregate approach proposed by the authors and presents the preliminary results of its implementation to a real test case using global position system (GPS) traces for extracting vehicle tours/trip chains which are available on a large scale for such uses as fleet management or vehicle insurance. The modeling framework was calibrated using a dataset of light goods vehicles operating in the Veneto region (Italy) for which automated vehicle monitoring (AVM) data were available. The results show that such an approach enables satisfactory results to be obtained in terms of freight distribution O-D matrices. [ABSTRACT FROM AUTHOR]

Properly extracting patterns of individual mobility with high resolution data sources such as the one extracted from smartphone applications offers important opportunities. Potential opportunities not offered by call detailed records (CDRs), which offer resolutions triangulated from antennas, are route choices, travel modes detection and close encounters. Nowadays, there is not a standard and large scale data set collected over long periods that allows us to characterize these. In this work we thoroughly examine the use of data from smartphone applications, also referred to as location-based services (LBS) data, to extract and understand the vehicular route choice behavior. Taking the Dallas-Fort Worth metroplex as an example, we first extract the vehicular trips with simple rules and reconstruct the origin-destination matrix by coupling the extracted vehicular trips of the active LBS users and the United States census data. We then present a method to derive the commonly used routes by individuals from the LBS traces with varying sample rate intervals. We further inspect the relation between the number of routes and the trip characteristics, including the departure time, trip length and travel time. Specifically, we consider the travel time index and buffer index for the LBS users taking different number of routes. Empirical results demonstrate that during the peak hours, travelers tend to reduce the impact of traffic congestion by taking alternative routes. Overall, the proposed data analysis framework is cost-effective to treat sparse data generated from the use of smartphones to inform routing behavior. The potential in practice is to inform demand management strategies, by targeting individual users while generating large scale estimates of congestion mitigation. [ABSTRACT FROM AUTHOR]

The last Brazilian agricultural frontier known as MATOPIBA, an acronym for the states of Maranhão, Tocantins, Piaui and Bahia, is a region that has stood out in the scenario of modern large-scale agriculture intensive in capital and technology. However, barriers related to the transportation system, especially regarding the high logistics costs, have been negatively impacting this sector. To optimize the performance of this chain, a model for minimizing transportation costs was developed, using linear programming with an origin-destination matrix. The purpose of this paper was to evaluate whether the use of the North-South Railroad has potential as a new alternative intermodal route for soybean export from the MATOPIBA region, in order to minimize the transportation costs and promote the viability of new ports located in the northeast region of the country. This configuration can also contribute to reduce the overload of Santos Port (SP). Two scenarios were proposed based on the 2016/17 harvest of soybeanwhere the first scenario (A) consisted only of road routes, while the second (B), in addition to these routes, incorporated road-rail and road-water way intermodal routes. As a result, total transportation cost was reduced by 30% in scenario B and about half of all cargo was shipped by the new intermodal road-rail route to the Itaqui Port in the Northeast region. This configuration promotes a more sustainable logistics chain for soy, with more rational use of transport modes and decentralization of investments to other regions. [ABSTRACT FROM AUTHOR]

ABSTRACT: The last Brazilian agricultural frontier known as MATOPIBA, an acronym for the states of Maranhão, Tocantins, Piaui and Bahia, is a region that has stood out in the scenario of modern large-scale agriculture intensive in capital and technology. However, barriers related to the transportation system, especially regarding the high logistics costs, have been negatively impacting this sector. To optimize the performance of this chain, a model for minimizing transportation costs was developed, using linear programming with an origin-destination matrix. The purpose of this paper was to evaluate whether the use of the North-South Railroad has potential as a new alternative intermodal route for soybean export from the MATOPIBA region, in order to minimize the transportation costs and promote the viability of new ports located in the northeast region of the country. This configuration can also contribute to reduce the overload of Santos Port (SP). Two scenarios were proposed based on the 2016/17 harvest of soybeanwhere the first scenario (A) consisted only of road routes, while the second (B), in addition to these routes, incorporated road-rail and road-water way intermodal routes. As a result, total transportation cost was reduced by 30% in scenario B and about half of all cargo was shipped by the new intermodal road-rail route to the Itaqui Port in the Northeast region. This configuration promotes a more sustainable logistics chain for soy, with more rational use of transport modes and decentralization of investments to other regions.

Small cities with less than 200,000 inhabitants do not usually suffer from chronic congestion problems. However, private vehicles are used excessively, making it necessary to implement measures to encourage further use of public transport and pedestrian mobility to make it more sustainable. Bypasses improve level of service (LOS) by removing cars from the city center, leading to significant reductions in overall travel time. Most studies so far have been conducted in large cities suffering chronic congestion problems, so the aim of this research is to analyze the effects of bypasses in small and non-congested cities through the construction of a traffic model in Badajoz (Spain), starting with the allocation of the origin-destination travel matrix derived from surveys and traffic counts conducted at the southern and eastern accesses. The traffic model describes the mobility in potentially-capturable future southern traffic relationships and allows insights into different alternatives in the construction of a new high LOS road. This research concludes that small cities with no chronic congestion problems should plan bypasses as close as possible to the city, since they are the most economical, produce greater traffic capture, greater time savings, and eliminate the largest number of CO2 emissions from the urban center. The more distant alternatives have a higher LOS, however, these are longer and more expensive solutions that also capture less traffic and thus eliminate less CO2 emissions.

In Italy, tourism is traditionally one of the most important driving sector of economic growth at the local level. The aim of the work is to explore the domestic inter-regional flow (the major share of Italian tourism) of the 20 Italian regions. This paper adopts a Multidimensional Scaling technique based on origin-destination matrix of domestic tourism flows. The findings point out geographical proximity, infrastructural factors and economic strength, in addition to specific place-based tourist resources, appears to be the determining factors of the competitive positioning. These results are useful to improve the further decision-making of different agents involved in tourism industry and the region’s capacity to attract domestic tourists.

A discrete choice model that consists of three sub-models was developed to investigates the route choice criteria of drivers who travel from their homes in the morning to the access point along the major streets that bound the Traffic Analysis Zones (TAZs). The first sub-model is a Nested Logit Model (NLM) that estimates the probability of a driver has or has no multiple routes, and if the driver has multiple routes, the route selection criteria are based on the access point’s intersection control type or other factors. The second sub-model is a Mixed Logit (MXL) model. It estimates the probabilities of the type of intersection control preferred by a driver. The third sub-model is a NLM that estimates the probabilities of a driver selecting his/her route for its shortest travel time or to avoid pedestrian, and if the aim is to take the fastest route, the decision criteria is based on the shortest distance or minimum stops and turns. Data gathered in a questionnaire survey were used to estimate the sub-models. The attributes of the utility functions of the sub-models are the driver’s demographic and trip characteristics. The model provides a means for transportation planners to distribute the total number of home-based trips generated within a TAZ to the access points along the major streets that bound the TAZ.

Travel demand forecasting in terms of origin-destination (O-D) matrix estimation on transportation networks is an important topic in transportation research. The motivation of each trip is the user’s desire of conducting a compulsory or noncompulsory activity. User activity choices should not be ignored for the trip demand forecasting; nevertheless, there are scare approaches of O-D matrix estimation incorporating users’ activity behavior in the literature. This study develops an activity-based bi-level model for the estimation of O-D matrix on road networks using the activity-based approach where road users’ activity and travel choices are integrated scheduled. In the estimation, the parameter of the user activity and travel choice model is simultaneously calibrated. A heuristic algorithm is explored to solve the model. A numerical example is provided. The example results illustrate that the developed model and heuristic algorithm are applicable and efficient tools to deal with the problem of O-D matrix estimation.

With the development of intelligent transportation systems, the estimation of traffic flow in urban areas has attracted a great attention of researchers. The timely and accurate travel information of urban residents could assist users in planning their travel strategies and improve the operational efficiency of intelligent transportation systems. Currently, the origin-destination (OD) flows of urban residents are formulated as an OD matrix, which is used to denote the travel patterns of urban residents. In this paper, a simple and effective model, called NMF-AR, is proposed for predicting the OD matrices through combining the nonnegative matrix factorization (NMF) algorithm and the Autoregressive (AR) model. The basic characteristics of travel flows are first revealed based on the NMF algorithm. Then, the nonlinear time series coefficient matrix, extracted from the NMF algorithm, is estimated based on the AR model. Finally, we predict OD matrices based on the estimated coefficient matrix and the basis matrix of NMF. Extensive experiments have been implemented, in collected real data about taxi GPS information in Beijing, for comparing our proposed algorithm with some known methods, such as different kinds of K-nearest neighbor algorithms, neural network algorithms and classification algorithms. The results show that our proposed NMF-AR algorithm have a more effective capability in predicting OD matrices than other models.

Transport agencies require accurate and updated information about public transport systems for the optimal decision-making processes regarding design and operation. In addition to assessing topology and service components, users’ behaviors must be considered. To this end, a data-driven performance evaluation based on passengers’ actual routes is key. Automatic fare collection platforms provide meaningful smart card data (SCD), but these are incomplete when gathered by entry-only systems. To obtain origin–destination (OD) matrices, we must manage complete journeys. In this paper, we use an adapted trip chaining method to reconstruct incomplete multi-modal journeys by finding spatial similarities between the outbound and inbound routes of the same user. From this dataset, we develop a performance evaluation framework that provides novel metrics and visualization utilities. First, we generate a space-time characterization of the overall operation of transport networks. Second, we supply enhanced OD matrices showing mobility patterns between zones and average traversed distances, travel times, and operation speeds, which model the real efficacy of the public transport system. We applied this framework to the Comunidad de Madrid (Spain), using 4 months’ worth of real SCD, showing its potential to generate meaningful information about the performance of multi-modal public transport systems.

As road traffic networks become more congested and information systems are implemented to manage traffic flows, real-time data gathering becomes increasingly important. Classic detectors are placed in one point of the network and are able to provide information only from that area. As useful as this is, it lacks the big picture of the routes the vehicles usually travel. There are applications developed to help individuals make their way into the road network, but these are no solutions that deal with the cause of traffic; rather, they counteract the effects. It becomes obvious that a proper management system, with knowledge of all the relevant aspects will better serve all travelers. The detection solution proposed in this paper is based on Bluetooth detectors. This system is able to match detected devices in the road network, filter the results, and generate a vehicle count that is proved to follow RADAR detection results.

The Origin-Destination (O-D) matrix, is an important information in transportation planning and traffic control. Rapid changes in land use, particularly in developing countries, have been and are on an increase, which makes the estimation and observation of this matrix more significant. The objective of this paper is to observe O-D matrix under two scenarios. In the first scenario, it is assumed that the traffic network is equipped with path-ID sensors. In this situation, the goal is to determine the optimal number and location of these sensors in the network, where by applying collected information through these sensors, the O-D matrix is observed. Because path-ID sensors are not available in many cities, in the second scenario the interview alternative is proposed in order to observe O-D matrix. The interview method has encountered some restrictions. Several mathematical programming models have been developed to overcome these restrictions. To illustrate these proposed methodologies, they are applied in the Nguyen-Dupuis transportation network and the results are analysed. By applying the model on the intercity road network in the Province of Isfahan (Iran), a large network, the efficiency of these proposed models is demonstrated. Finally, some conclusions and final recommendations are included. [ABSTRACT FROM AUTHOR]

• New data driven framework for OD matrix estimation. • No equilibrium assignment nor network loading model needed. • Scalable for large networks using dimensionality reduction techniques. • Supervised learning to estimate production and attraction from 3D supply patterns. • Results on small and large network promising. The fundamental challenge of the origin-destination (OD) matrix estimation problem is that it is severely under-determined. In this paper we propose a new data driven OD estimation method for cases where a supply pattern in the form of speeds and flows is available. We show that with these input data, we do not require an iterative dynamic network loading procedure that results in an equilibrium assignment, nor do we need an assumption on the kind of equilibrium that emerges from this process. The minimal number of ingredients which are needed are (a) a method to estimate/predict production and attraction time series; (b) a method to compute the N shortest paths from each OD zone to the next; and (c) two—possibly OD-specific—assumptions on the magnitude of N ; and on the proportionality of path flows between these origins and destinations, respectively. The latter constitutes the most important behavioral assumption in our method, which relates to how we assume travelers have chosen their routes between OD pairs. We choose a proportionality factor that is inversely proportional to realized travel time, where we incorporate a penalty for path overlap. For large networks, these ingredients may be insufficient to solve the resulting system of equations. We show how additional constraints can be derived directly from the data by using principal component analysis, with which we exploit the fact that temporal patterns of production and attraction are similar across the network. Experimental results on a toy network and a large city network (Santander, Spain) show that our OD estimation method works satisfactorily, given a reasonable choice of N , and the use of so-called 3D supply patterns, which provide a compact representation of the supply dynamics over the entire network. Inclusion of topological information makes the method scalable both in terms of network size and for different topologies. Although we use a neural network to predict production and attraction in our experiments (which implies ground-truth OD data were needed), there are straight-forward paths to improve the method using additional data, such as demographic data, household survey data, social media and or movement traces, which could support estimating such ground-truth baseline production and attraction patterns. The proposed framework would fit very nicely in an online traffic modeling and control framework, and we see many paths to further refine and improve the method. [ABSTRACT FROM AUTHOR]

In this paper, a Markov chain origin-destination matrix estimation problem is investigated in which the average time between two incoming streams to or outgoing streams from nodes in consecutive time periods is considered as a Markov chain. Along with, a normal distribution with pre-determined parameters in each period is considered for traffic counts on links. A bi-level programming problem is introduced where in its upper level the network flow pattern in the n th period is estimated so that the probability of the estimated traffic counts is maximized, while in the lower level a traffic assignment problem with the equilibrium conditions is solved. We reduce the proposed nonlinear bi-level model to a new one level linear programming problem, where by using a trust-region method the local optimal solutions are obtained. Some numerical examples are provided to illustrate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

The work is devoted to the development of new methods and algorithms to support decision making when planning air travel using uncertainties in the form of fuzzy numbers. The proposed approach makes it possible to define rational methods of choice: how to change the transport graph to better meet the needs of the population. This is particularly relevant in the context of the reduced demand for air travel caused by the pandemic and the need to switch from large to smaller aircraft types. The problem is solved by restoring the fuzzy origin–destination matrix of current statistics on air traffic between airports. The problem is that we do not know what proportion of passengers moving between the specified points are forced to use large transport hubs as intermediate destinations. To determine the validity of the origin–destination matrix, we build a number of optimization models to determine fuzzy intervals and search for correspondence with the maximum value of the membership function. Algorithmic and software search for the fuzzy origin–destination matrix and fuzzy ranking of potentially promising routes are developed. The perspective of the given approach is shown by an example of a task concerning a choice of new routes between regional airports.

This study demonstrates the use of mobile phone data to derive country-wide mobility patterns. We identified significant locations of users such as home, work, and other based on a combined measure of frequency, duration, time, and day of mobile phone interactions. Consecutive mobile phone records of users are used to identify stay and pass-by locations. A stay location is where users spend a significant amount of their time measured through their mobile phone usage. Trips are constructed for each user between two consecutive stay locations in a day and then categorized by purpose and time of the day. Three measures of entropy are used to further understand the regularity of user’s spatiotemporal mobility patterns. The results show that user’s in a high entropy cluster has high percentage of non-home based trips (77%), and user’s in a low entropy cluster has high percentage of commuting trips (49%), indicating high regularity. A set of doubly constrained trip distribution models is estimated. To measure travel cost, the concept of a centroid point that assumes the origins and destinations of all trips are concentrated at an arbitrary location such as the centroid of a zone is replaced by multiple origins and destinations represented by cell tower locations. Note that a cell tower location can only be used as trips origin/destination location when a stay is detected. The travel cost measured between cell tower locations has resulted in shorter trip distances and the model estimation shows less sensitivity to the distance-decay effect.