Search Results (38)

This paper presents the novel, smart, commutable, and self-regulated SSF-Roundabout as one of the potential solutions in the environment of smart mobility. The SSF-Roundabout implements traffic counting systems, smart cameras, LED road markers, and Variable Message Signs (VMS) on arms. Based on the instantaneous detection of the traffic demand level, vehicles can be properly channelled or not into right-turn bypass lanes, which the roundabout is equipped with in every arm, to guarantee the requested capacity, Level of Service (LOS), and safety. In total, fifteen very different layout configurations of the SSF-Roundabout are available. Several traffic analyses were performed by using ad hoc traffic engineering closed-form models and case studies based on many origin-destination traffic matrices (M_O/D(t)) and proportions of CAVs in the traffic stream (from 0% to 100%). Simulation results demonstrate the correlation between layout scenarios, traffic intensity, distribution among arms, and composition in terms of CAVs and their impact on entry and total capacity, control delay, and LOS of the SSF-Roundabout. For instance, the right-turn bypass lane activation may produce an entry capacity increase of 48% and a total capacity increase of 50% in the case of 100% of CAVs in traffic streams. Full article

The growing complexity of urban mobility necessitates more adaptive, data-driven approaches to transport demand forecasting. This study incorporates anonymized Call Detail Record (CDR) data—originally collected for mobile network billing—into the conventional four-step travel demand model to more accurately estimate trip behavior. Employing a stay-based method, significant user locations are identified, and individual mobility patterns are reconstructed. These patterns are then aggregated at the zonal level and validated against a large-scale household survey conducted in Sri Lanka. The proposed framework enables the extraction of origin–destination matrices and supports route assignment using CDR data, demonstrating a strong correlation with traditional survey results. This research highlights the potential of repurposed CDR data as a scalable, cost-efficient alternative to conventional travel surveys for estimating travel demand. Full article

The rapid expansion of urban rail networks has resulted in increasingly complex passenger flow patterns, presenting significant challenges for operational management, especially during incidents and emergencies. Disruptions such as power equipment failures, trackside faults, and train malfunctions can severely impact transit efficiency and reliability, leading to congestion and cascading network effects. Existing models for predicting passenger origin–destination (OD) matrices struggle to provide accurate and timely predictions under these disrupted conditions. This study proposes a deep counterfactual inference model that improves both the prediction accuracy and interpretability of OD matrices during incidents. The model uses a dual-channel framework based on multi-task learning, where the factual channel predicts OD matrices under normal conditions and the counterfactual channel estimates OD matrices during incidents, enabling the quantification of the spatiotemporal impacts of disruptions. Our approach which incorporates KL divergence-based propensity matching enhances prediction accuracy by 4.761% to 12.982% compared to baseline models, while also providing interpretable insights into disruption mechanisms. The model reveals that incident types vary in delay magnitude, with power equipment incidents causing the largest delays, and shows that incidents have time-lag effects on OD flows, with immediate impacts on origin stations and progressively delayed effects on destination and neighboring stations. This research offers practical tools for urban rail transit operators to estimate incident-affected passenger volumes and implement more efficient emergency response strategies, advancing emergency response capabilities in smart transit systems. Full article

Understanding how people spend time on daily activities is key to modeling travel behavior. However, accurately estimating the duration of these activities remains a significant challenge, especially when generating synthetic activity-travel data. This article introduces an activity-based approach that addresses this issue by applying statistical and machine learning models to improve the precision of activity duration estimates. The method utilizes real-world Origin-Destination (OD) datasets to generate additional synthetic data that can support transportation planning processes. Unlike conventional approaches that rely solely on OD matrices, this framework incorporates Cox and Cox-based hazard models to more precisely estimate activity durations, as well as arrival and departure times across trip segments. Statistical tests and comparative evaluations show that the proposed method produces more accurate synthetic data than existing open-source tools that do not employ hazard-based modeling. A case study using real-world data from Athens, Greece, demonstrates the effectiveness of the proposed approach. Full article

Regional planning agencies increasingly rely on Spatial Economic Models (SEMs) to evaluate the impact of various policies. However, traditional SEMs often employ homogeneous technical coefficients (TCs) to represent technology patterns used by activities located in very different areas of a region, leading to misrepresentations of production and consumption behaviors, and consequently, inaccurate modeling results. To this end, we propose a Differential Spatial Economic Modeling (DSEM) framework that incorporates region-specific TCs for activities within Independent Planning Units (IPUs), such as provinces or cities, each characterized by unique economic, demographic, and technological features. The DSEM framework comprises three core components: (1) a regional economy model that forecasts activity totals for each IPU using economic and demographic forecasting model, supplemented by statistical analyses like the Gini index and K-means clustering to group activities from different IPUs into homogeneous ‘technology’ clusters based on their TCs; (2) a land use model that allocates IPU activity totals to corresponding traffic analysis zones (e.g., counties or districts) using the Differential Spatial Activity Allocation (DSAA) method. This determines the spatial distribution of commodities (such as goods, services, floor space, and labor) across exchange zones, balancing supply and demand to achieve spatial equilibrium in both quantity and price; and (3) a transport model that performs modal split and network assignment, distributing commodity trip origin–destination matrices across a multimodal transportation supernetwork (highways, railways, and waterways) using a probit-based stochastic user equilibrium assignment model. The proposed method is applied to a case study of the Yangtze River Economic Belt, China. The results demonstrate that the proposed DSEM yields better goodness-of-fit (R²) values between observed and estimated flows compared to the traditional aggregate SEM. This indicates a more precise and objective representation of spatial economic activities and technological patterns, thus resulting in improved estimates of freight flows for individual transportation modes and specific links. Full article

Accurate estimation of passenger origin–destination (OD) matrices is critical for optimizing public transportation systems, yet conventional methods face challenges, such as incomplete alighting data, high infrastructure costs, and privacy concerns. With existing GPS sensors and the additional deployment of a single low-cost Bluetooth sensor (10–20 US dollars) per bus, the proposed method can derive passenger OD flow without requiring passengers to tap in or tap out. The GPS sensor updates the bus locations, and the Bluetooth sensor receives signals from surrounding devices, including those onboard devices and nearby external devices. A Fuzzy C-Means clustering algorithm was employed to differentiate passenger and non-passenger devices based on detected indicators, such as detection frequency, signal strength, vehicular mobility, etc. Validation on Shanghai’s Fengpu BRT line demonstrated 91.22–96.02% accuracy in boarding proportion estimation and 95.18–95.52% for alighting during peak hours. Compared to the historical data-based method, the proposed method achieved higher similarity to ground truth and reduced the mean squared error by 12.89–69.95%. Full article

One of the biggest challenges in designing a logistics network is predicting the demand flows between all pairs of points in the network. Currently, the gravity model is mainly used for estimating the demand flow between points. However, the gravity model uses historical data to estimate values for its multiple parameters and distance between pairs to forecast the demand flow. Distance values close to zero and unprecedented changes in demand flow data create numerical instability for the gravity model’s output. Hence, the proximity factor, a single parameter model that uses the relative ordering of pairs instead of distance, was developed. In this paper, we systematically compare the proximity factor and the gravity model. It is shown that the proximity factor is a robust in terms of reliability and competitive alternative to the gravity model. According to our analysis, the proximity factor model can replace the gravity model in some applications when no historical data are available to adjust the parameters of the latter. Full article

The origin–destination (OD) matrix is a critical tool in understanding human mobility, with diverse applications. However, constructing OD matrices can pose significant privacy challenges, as sensitive information about individual mobility patterns may be exposed. In this paper, we propose DistOD, a hybrid privacy-preserving and distributed framework for the aggregation and computation of OD matrices without relying on a trusted central server. The proposed framework makes several key contributions. First, we propose a distributed method that enables multiple participating parties to collaboratively identify hotspot areas, which are regions frequently traveled between by individuals across these parties. To optimize the data utility and minimize the computational overhead, we introduce a hybrid privacy-preserving mechanism. This mechanism applies distributed differential privacy in hotspot areas to ensure high data utility, while using localized differential privacy in non-hotspot regions to reduce the computational costs. By combining these approaches, our method achieves an effective balance between computational efficiency and the accuracy of the OD matrix. Extensive experiments on real-world datasets show that DistOD consistently provides higher data utility than methods based solely on localized differential privacy, as well as greater efficiency than approaches based solely on distributed differential privacy. Full article

Estimating origin-destination (OD) demand is integral to urban, regional, and national freight transportation planning and modeling systems. However, in developing countries, existing studies reveal significant inconsistencies between OD estimates for domestic and import/export commodities derived from interregional input-output (IO) tables and those from regional IO tables. These discrepancies create a significant challenge for properly forecasting the freight demand of regional/interregional multimodal transportation networks. To this end, this study proposes a novel integrated framework for estimating regional and international (import/export) OD freight flows for a set of key commodities that dominate long-distance transportation. The framework leverages multisource data and follows a three-step process. First, a spatial economic model, PECAS activity allocation, is developed to estimate freight OD demand within a specific region. Second, the international (import and export) freight OD is estimated from different zones to foreign countries, including major import and export nodes such as international seaports, using a gravity model with the zone-pair friction obtained from a multimodal transportation model. Third, the OD matrices are converted from monetary value to tonnage and assigned to the multimodal transportation super network using the incremental freight assignment method. The model is calibrated using traffic counts of the highways, railways, and port throughput data. The proposed framework is tested through a case study of the Province of Jiangxi, which is crucial for forecasting freight demand before the planning, design, and operation of the Ganyue Canal. The predictive analytics of the proposed framework demonstrated high validity, where the goodness-of-fit (R²) between the observed and estimated freight flows on specific links for each of the three transport modes was higher than 0.9. This indirectly confirms the efficacy of the model in predicting freight OD demands. The proposed framework is adaptable to other regions and aids practitioners in providing a comprehensive tool for informed decision-making in freight demand modeling. Full article

The analysis and planning of suburban traffic flows is an extremely important task to ensure efficient and fluent traffic distribution in existing infrastructure. One of the main sources of this information from existing situations is origin–destination (OD) matrices, usually obtained from traffic CCTV cameras. In this paper, a novel method is proposed for the filtration of transit traffic from the overall traffic through the area. The main feature that differentiates this method from existing outlier (and thus transit traffic) detection methods is that it is focused solely on prolonged trips (which are believed to be caused by vehicles stopping in the investigated area). Initial calibration of the method on training data (sample size of N = 216,159 trips through paired detectors) and verification on test data prove the accuracy of the algorithm on the level of 98% in urban and suburban areas, respectively, in Czech Republic conditions, which gives high hopes for the feasibility of the method. Full article

In many real-world situations, the available data consist of a set of several asymmetric pairwise proximity matrices that collect directed exchanges between pairs of objects measured or observed in a number of occasions (three-way data). To unveil patterns of exchange, a clustering model is proposed that accounts for the systematic differences across occasions. Specifically, the goal is to identify the groups of objects that are primarily origins or destinations of the directed exchanges, and, together, to measure the extent to which these clusters differ across occasions. The model is based on two clustering structures for the objects, which are linked one-to-one and common to all occasions. The first structure assumes a standard partition of the objects to fit the average amounts of the exchanges, while the second one fits the imbalances using an “incomplete” partition of the objects, allowing some to remain unassigned. In addition, to account for the heterogeneity of the occasions, the amounts and directions of exchange between clusters are modeled by occasion-specific weights. An Alternating Least-Squares algorithm is provided. Results from artificial data and a real application on international student mobility show the capability of the model to identify origin and/or destination clusters with common behavior across occasions. Full article

Short-term origin–destination (OD) prediction in urban rail transit (URT) is vital for improving URT operation. However, due to the problems such as the unavailability of the OD matrix of the current day, high dimension and long-range spatio-temporal dependencies, it is difficult to further improve the prediction accuracy of an OD matrix. In this paper, a novel spatio-temporal self-attention network (SSNet) for OD matrix prediction in URT is proposed to further improve the prediction accuracy. In the proposed SSNet, a lightweight yet effective spatio-temporal self-attention module (STSM) is proposed to capture complex long-range spatio-temporal dependencies, thus helping improve the prediction accuracy of the proposed SSNet. Additionally, the finished OD matrices on previous days are used as the only data source without the passenger flow data on the current day in the proposed SSNet, which makes it possible to predict the OD matrices of all time intervals on the current day before the operation of the current day. It is demonstrated by experiments that the proposed SSNet outperforms three advanced deep learning methods for short-term OD prediction in URT, and the proposed STSM plays an important role in improving the prediction accuracy. Full article

Due to the inherent difficulty in direct observation of traffic demand (including generation, attraction, and assignment), the estimation of origin–destination (OD) poses a significant and intricate challenge in the realm of Intelligent Transportation Systems. As the state-of-the-art methods usually focus on a single traffic demand distribution, accurate estimation of OD in the face of diverse traffic demand and road structures remains a formidable task. To this end, this study proposes a novel model, Cyclic Quasi-Dynamic Transformers (CQDFormer), which leverages forward and backward neural networks for effective OD estimation and traffic assignment. The employment of quasi-dynamic assumption and self-attention mechanism enables CQDFormer to capture the diverse and non-linear characteristics inherent in traffic demand. We utilize calibrated simulations to generate traffic count-OD pairwise data. Additionally, we incorporate real prior matrices and traffic count data to mitigate the distributional shift between simulation and the reality. The proposed CQDFormer is examined using Simuation of Urban Mobility (SUMO), on a large-scale downtown area in Haikou, China, comprising 2328 roads and 1171 junctions. It is found that CQDFormer shows satisfied convergence performance, and achieves a reduction of RMSE by $46.98\%$, MAE by $45.40\%$ and MAPE by $29.76\%$, in comparison to the state-of-the-art method with the best performance. Full article

Spatial autocorrelation analysis is essential for understanding the distribution patterns of spatial flow data. Existing methods focus mainly on the origins and destinations of flow units and the relationships between them. These methods measure the autocorrelation of gravity or the positional and directional autocorrelations of flow units that are treated as objects. However, the intrinsic complexity of actual flow data necessitates the consideration of not only gravity, positional, and directional autocorrelations but also the autocorrelations of the variables of interest. This study proposes a global spatial autocorrelation method to measure the variables of interest of flow data. This method mainly consists of three steps. First, the proximity constraints of the origin and destination of a flow unit are defined to ensure similarity of flow units in terms of direction, distance, and position. This undertaking aims to determine the neighborhood of flow units and generate their adjacent matrices. Second, a spatial autocorrelation measurement model for flow data is constructed on the basis of the adjacent matrix generated. Artificial data sets are also employed to test the validity of the model. Finally, the proposed method is applied to the flow data analysis of population migration in central and eastern China to prove the practical application value of the model. The proposed method is universal and can be generalized to the global spatial autocorrelation analysis of any type of flow data. Full article

Mobile phones have achieved a high rate of penetration and gained great interest in the field of travel behavior studies. However, mobile phone data exploitation for national travel models has only been sporadically studied thus far. This work focuses on one of the most extensive cellular surveys of its kind carried out thus far in the world, which was performed for two years between 2018 and 2019 with the participation of the two largest cellular providers in Israel, as well as leading GPS companies. The large-scale cell phone survey covered half the population using cellphones aged 8+ in Israel and uncovered local and national trip patterns, revealing the structure of nationwide travel demand. The methodology consists of the following steps: (1) plausibility and quality checks for the data of the mobile operators and the GPS data providers; (2) algorithm development for trip detection, home/work location detection, location and time accuracy, and expansion factors; (3) accuracy test of origin–destination matrices at different resolutions, revisions of algorithms, and reproduction of data; and (4) validation of results by comparison to reliable external data sources. The results are characterized by high accuracy and representativeness of demand and indicate a strong correlation between the cellular survey and other reliable sources. Full article