Origin Destination Research Articles

Passenger Flow Path Prediction Based on Urban Rail Transit AFC Data: An Example of Chengdu, China

The development of the automatic fare collection (AFC) systems provides significant support for predicting passenger flow on urban rail transit. This paper extracts passenger travel patterns using AFC data on urban rail transit in Chengdu, China, over a one-month period. Passengers are divided into two categories based on their travel habits and data mining models, and multinomial logit (MNL) models are separately used to predict their destinations. Furthermore, a two-way search algorithm is developed to search the optimal paths between origin-destination (OD) pairs by considering interchange constraints. Start a path search through the origin point and destination point, respectively, until the shortest path is found. The maximum effectiveness of a path is measured by travel time, interchange time, and the number of interchanges between the OD pairs. Finally, the validity of the proposed passenger flow path prediction method is verified by using the AFC data of Chengdu metropolitan rail transit from April 2018.

Bi-level model predictive control for metro networks: Integration of timetables, passenger flows, and train speed profiles

This paper deals with the train scheduling problem for metro networks taking into account time-dependent passenger origin–destination demands and train speed profiles. The aim is to adjust train schedules online according to time-dependent passenger demands so that passenger satisfaction and operational costs are jointly optimized. An extended passenger absorption model that explicitly includes time-dependent passenger origin–destination demands is developed, where the term “absorption” refers to passengers boarding trains. Then, the passenger absorption model is extended to a bi-level framework, where passenger demands and rolling stock availability are considered at the higher level, and detailed timetables and train speed profiles are included at the lower level. A bi-level model predictive control (MPC) approach is developed for the integrated problem. The optimization problems of both levels of the bi-level MPC approach can be converted into mixed-integer linear programming (MILP) problems, which enables us to solve them with existing MILP solvers. We then show that the recursive feasibility of both the higher-level and the lower-level optimization problems can be guaranteed. In this way, we can achieve real-time train scheduling for the metro system. Numerical experiments, based on real-life data from the Beijing metro network, illustrate the effectiveness of the extended passenger absorption model and the proposed bi-level MPC approach.

Multi-Task Weakly Supervised Learning for Origin–Destination Travel Time Estimation

Travel time estimation from GPS trips is of great importance to order duration, ridesharing, taxi dispatching, etc. However, the dense trajectory is not always available due to the limitation of data privacy and acquisition, while the origin-destination (OD) type of data, such as NYC taxi data, NYC bike data, and Capital Bikeshare data, is more accessible. To address this issue, this paper starts to estimate the OD trips travel time combined with the road network. Subsequently, a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</u> ulti-task <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">W</u> eakly <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> upervised <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> earning Framework for <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> ravel <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> ime <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u> stimation (MWSL-TTE) has been proposed to infer transition probability between roads segments, and the travel time on road segments and intersection simultaneously. Technically, given an OD pair, the transition probability intends to recover the most possible route. And then, the output of travel time is equal to the summation of all segments' and intersections' travel time in this route. A novel route recovery function has been proposed to iteratively maximize the current routes' co-occurrence probability, and minimize the discrepancy between routes' probability distribution and the inverse distribution of routes' estimation loss. Moreover, the expected log-likelihood function based on a weakly-supervised framework has been deployed in optimizing the travel time from road segments and intersections concurrently. We conduct experiments on a wide range of real-world taxi datasets in Xi'an and Chengdu and demonstrate our method's effectiveness on route recovery and travel time estimation.

Book-ahead ride-hailing trip and its determinants: Findings from large-scale trip records in China

The cruising of ride-hailing vehicles generates negative externalities such as traffic congestion and vehicular emissions. These externalities can be mitigated by reducing cruising driving via operating book-ahead ride-hailing services, where the platform dispatches and routes drivers based on precise information on travelers’ departure time and origin–destination (OD). However, the effects of factors influencing book-ahead ride-hailing trips have rarely been empirically examined with real data. Using six-month trip data from China, this study employs a gradient boosting decision tree (GBDT) method with hyperparameters optimized by the Bayesian optimization algorithm to examine the factors associated with book-ahead ride-hailing trips across OD pairs (hexagon cells-to-hexagon cells) at various spatial scales. The relative importance rankings generated from this study indicate that trip features, weather conditions, and accessibility to transportation hubs are significant determinants correlated with the usages of book-ahead ride-hailing. The partial dependence plots demonstrate the nonlinear threshold effects of these determinants on the hourly number of book-ahead ride-hailing per OD pair. Moreover, this study compares the differences in associations between peak and non-peak hours as well as weekdays and weekends. The disparity in the nonlinear threshold effects between weekdays and weekends is only observable during the evening peak period and not at other times. These findings provide valuable insights into developing practical strategies for promoting book-ahead ride-hailing services.

Vehicle Traffic Volume and Origin Destination Characteristics for Erbil Rural Highways

Vehicle Traffic Volume and Origin Destination Characteristics for Erbil Rural Highways

The maximal detour liner shipping hub location problem: Improving the applicability of the p-hub center problem

This paper investigates a novel maximal detour hub location problem arising in liner shipping, referred to as a maximal detour liner shipping hub location problem, which can be regarded as a novel p-hub center problem. Different from conventional formulations of the p-hub center problem, both cost and service can be kept at a good level based on our investigated problem, although economic objectives are not considered in our problem. By considering origin–destination (OD) path based detour and sub-path based detours, we propose three mixed-integer linear programming models for our problem. The proposed formulations with sub-path based detours can be efficiently solved by CPLEX. To efficiently solve our formulation with OD path based detour, we propose an improved Benders decomposition algorithm, where the inspection strategy, the heuristic strategy and the multi-cut strategy are developed for improving algorithm efficiency. Finally, we provide numerical experiments to account for the effectiveness and efficiency of the proposed formulations and algorithm.

Identification of homogenous zones using riding speed time-series to reveal the spatial structure of the urban pace

Traffic speed is essential for the measurement of urban pace. The riding speed metric is one of the most effective ways to identify the underlying dynamic patterns and structures of urban pace. However, few studies have explored the patterns of urban pace reflected by riding speed differentiation of public shared bicycle groups. To this end, this study first infers the riding speeds under unknown path conditions based on the origin–destination of large sample riding records in Suzhou, China. Second, an identification model of homogenous zones for urban pace is established by neighboring station search and time-series similarity-based neighboring station merger. Finally, the fluctuation and deviation indexes of the fitted rhythm curves are adopted to measure the heterogeneity degree of the urban pace in each homogenous zone. Results show that the urban paces based on riding speed have significant spatial differentiation characteristics in the city, and the generated homogeneous zones show a certain stable structure in urban space. The old town has the fastest urban pace and is at a consistently high level, the new district has a faster urban pace and a double-peak pattern, the suburb area has a moderate urban pace and shows a single-peak pattern, while the peripheral area has the slowest urban pace with no significant pace spikes. Meanwhile, the waveform of the urban pace in different zones have a delay of approximately one hour. This study proposes an effective approach to reflect the spatiotemporal homogeneity and heterogeneity of urban pace through the dynamic traffic flow elements, which can be universally applied to other cities and is helpful for planning more dynamic urban functional areas and detecting life stress of urban residents.

Day-to-day traffic control for networks mixed with regular human-piloted and connected autonomous vehicles

The emerging connected autonomous vehicle (CAV) technology offers a significant opportunity to address the congestion problem caused by the selfish routing behavior of human drivers. If the transportation management center (TMC) can control the routing of all vehicles on the network, they could easily reverse this effect. However, during the transition period, CAVs will share roads with human-piloted vehicles (HVs). In the era of mixed autonomy, a more realistic scenario is to route the CAVs to lead the traffic state towards the desired equilibrium. We first consider a repeated routing game in which the TMC can control the routes of CAVs to minimize the system travel time, i.e., CAVs follow the system optimal (SO) routing principle. In this manner, we can influence the travel time traversing each link, which indirectly influences HV users’ route choices. As a consequence, HV users may adjust their routes dynamically through day-to-day (DTD) learning to minimize individual travel cost. On the other hand, the SO routing strategy may induce higher travel time for CAVs than HVs within the same origin–destination (OD) pair. To address such inherent unfairness, road pricing is used to influence route choices of HVs to achieve specific supply regulation targets, e.g., traffic restraint. We present qualitative analysis on the stability of the controlled DTD dynamics and the effect of road pricing regarding the unfairness. To the best of our knowledge, this paper is one of the first to design dynamic pricing schemes for a mixed autonomy system under DTD learning dynamics. We carry out numerical experiments to demonstrate the effectiveness of the control schemes. The cooperative CAVs could reduce the total travel time when the market penetration rate of CAVs reaches the lower minimum control ratio that the network could escape the user equilibrium (UE) condition. When the market penetration rate of CAVs reaches the upper minimum control ratio, the network would achieve SO condition even though there are non-cooperative HVs. On the other hand, the pricing scheme can reduce the unfairness index and total travel time of the network when the market penetration rate of CAVs is low.

CQDFormer: Cyclic Quasi-Dynamic Transformers for Hourly Origin-Destination Estimation

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.

Assessing transportation network redundancy by integrating route diversity and spare capacity

In this paper, we develop a new transportation network redundancy measure by integrating the route diversity and spare capacity dimensions, which have been demonstrated as two complementary dimensions of redundancy and assessed separately in the literature. Instead of modelling redundancy as two separate measures with significantly different units and scales, an integrative measure is beneficial for comprehensive comparison and selection of redundancy enhancement schemes. Specifically, we represent the transportation network by adding a virtual link directly connecting each origin-destination (O-D) pair. The virtual link choice probability is affected by both the route diversity of the O-D pair and the spare capacity of these routes, which is used in the proposed measure. Numerical examples in the realistic Winnipeg network are conducted. The effectiveness of the proposed measure is further verified by applying it to identify the critical links in the Shanghai Expressway Network, where a severe traffic accident happened in 2016.

An origin–destination passenger flow prediction system based on convolutional neural network and passenger source-based attention mechanism

An accurate origin–destination (OD) passenger flow prediction system is crucially important for urban metro operation and management. However, there are still lacking targeted prediction systems focusing on the passenger travel demand causing urban metro oversaturation. In this study, we first identify the passenger sources of the oversaturated sections and pinpoint the key part of passenger travel demand causing major oversaturation. Next, we take advantage of the attention mechanism and the masked loss function to incorporate the passenger source information into the prediction model and develop a passenger source attention mechanism based convolutional neural network (PSAM-CNN) model. Finally, the developed PSAM-CNN model is validated using the actual passenger travel demand data of Shenzhen Metro. Comparing with several state-of-art benchmark models, the PSAM-CNN model can predict the OD passenger flows causing urban metro oversaturation more accurately, providing more targeted and accurate OD flow information for improving the operation and management of urban metro.

Modeling virus transmission risks in commuting with emerging mobility services: A case study of COVID-19

Commuting is an important part of daily life. With the gradual recovery from COVID-19 and more people returning to work from the office, the transmission of COVID-19 during commuting becomes a concern. Recent emerging mobility services (such as ride-hailing and bike-sharing) further deteriorate the infection risks due to shared vehicles or spaces during travel. Hence, it is important to quantify the infection risks in commuting. This paper proposes a probabilistic framework to estimate the risk of infection during an individual’s commute considering different travel modes, including public transit, ride-share, bike, and walking. The objective is to evaluate the probability of infection as well as the estimation errors (i.e., uncertainty quantification) given the origin-destination (OD), departure time, and travel mode. We first define a general trip planning function to generate trip trajectories and probabilities of choosing different paths according to the OD, departure time, and travel mode. Then, we consider two channels of infections: 1) infection by close contact and 2) infection by touching surfaces. The infection risks are calculated on a trip segment basis. Different sources of data (such as smart card data, travel surveys, and population data) are used to estimate the potential interactions between the individual and the infectious environment. A first-order approximation is used to simplify the computational complexity. We also derive the closed-form formulation for the estimation errors, enabling us to quantify the uncertainty of the estimation results. The model is implemented in the MIT community as a case study. We evaluate the commute infection risks for employees and students. Results show that most of the individuals have an infection probability close to zero. The maximum infection probability is around 1.3%, implying that the probability of getting infected during the commuting process is low. Individuals with larger travel distances, traveling in transit, and traveling during peak hours are more likely to get infected. Practical implementations of the model are also discussed.

An origin-destination level analysis on the competitiveness of bike-sharing to underground using explainable machine learning

Bike-sharing offers a convenient transportation option, enhancing the potential for direct competition with underground transportation, especially for short-distance trips. However, research on bike-sharing trips primarily focuses on survey data or aggregated data at the station-level. Few attempts have been made to understand the competition between bike-sharing and underground at the origin-destination (OD) level. This study aims to explore the competitiveness of bike-sharing to the underground at short-distance level using actual OD-level bike-sharing and underground ridership data collected in London. Light Gradient Boosting Machine and SHapley additive explanations models are employed for the analysis.Our results found that bike-sharing can serve as a competitor to the underground, especially in denser urban areas and peak periods. The competitiveness of bike-sharing is associated with the attributes of trips' origins and destinations, route characteristics, and time. In particular, the route characteristics of travel duration/distance, road gradient, bike infrastructure availability and the number of crossings are correlated with the competitiveness of bike-sharing to the underground. Moreover, it is found that users pay more attention to the characteristics of origins rather than destinations. Our findings can provide valuable implications for promoting bike-sharing as a substitution to underground service.

Restoring culture and capital to cultural capital: origin–destination cultural distance and immigrant earnings in the United States

ABSTRACT An extensive sociological literature maintains that cultural capital is pivotal in perpetuating social inequalities. However, empirical tests of cultural capital theory focus on how culture influences educational outcomes, not earnings, and they mainly look for cultural differences across social classes within societies. We propose a direct test of economic returns to cultural capital based instead on differences in national cultures across countries. Using the American Community Survey and the National Survey of College Graduates, we analyze the relationship between immigrants’ lack of U.S.-specific cultural capital, proxied by cultural distance between the origin country and the U.S., and their earnings. Findings consistently indicate that origin – U.S. cultural distance is linked to immigrants’ lower earnings after controlling for numerous other factors, supporting cultural capital theory. Cultural distance earnings penalties are more pronounced for immigrants with at least a bachelor’s degree, those arriving in adulthood, and those with foreign degrees. Moreover, county-level analysis reveals more sizable cultural distance penalties in more competitive and unequal labour markets, highlighting how subnational receiving contexts shape origin-country disparities in immigrants’ economic incorporation at their destinations.

A Multilayer Network Approach for the Bimodal Bus–Pedestrian Line Planning Problem

In this paper, we formulate and solve the urban line planning problem considering a multilayer representation of a bimodal transportation network. Classical formulations are usually constructed over a planar network, which implies the need to introduce several strong non-linearities in terms of frequencies when modeling transfer times. In the proposed network representation, each candidate line is stored in a specific layer and the passengers’ movements for each origin–destination pair are modelled considering a strategy subgraph, contributing to a sparse model formulation that guarantees feasibility and simplifies the assignment process. The methodology is first tested using the Mandl network, obtaining results that are comparable in terms of quality with the best metaheuristic approaches proposed in the scientific literature. With the aim of testing its applicability to large scenarios, the proposed approach is then used to design the main urban transit network of Seville, a large scenario with 141 nodes and 454 links, considering artificial unfavorable demand data. The reasonable computation time required to exactly solve the problem to optimality confirms the possibility of using the multilayer approach to deal with multimodal network design strategic problems.

Mapping hazardous locations on a road network due to extreme gross vehicle weights

This study investigates the application of hazard maps in identifying critical locations associated with extreme vehicle load events. Examining extreme vehicle loads can offer valuable insights into the reliability of road infrastructure. Weigh-In-Motion (WIM) systems are commonly employed for this purpose; however, their limited availability often necessitates the use of less-sophisticated traffic counters (LSTCs). Unfortunately, LSTCs are unable to measure vehicular axle loads, posing a significant drawback. To overcome this limitation, we propose a methodology that utilizes data from LSTCs to estimate axle loads and map extreme gross vehicle weights. To demonstrate the feasibility of this approach, we present a case study concentrating on the major highway corridors in Mexico. While WIM stations are absent, data from a network of 1,777 counting stations and origin–destination surveys are available. By quantifying Gaussian copula-based Bayesian networks using the existing information, we generate synthetic site-specific axle loads. Subsequently, we calculate gross vehicle weights for selected return periods. These study findings facilitate the identification of hazardous locations. Additionally, we provide an interactive web map and a graphical user interface to generate synthetic axle loads. These tools, along with the proposed methodology, can serve as the foundation for maintenance strategies for existing roads and bridges.

The impact of remote work on green space values in regional housing markets

We examine the extent to which the increased prevalence of work from home (WFH) due to the COVID-19 pandemic has made green amenities more desirable.22Data provided by Zillow through the Zillow Transaction and Assessment Dataset (ZTRAX). More information on accessing the data can be found at http://www.zillow.com/ztrax. The results and opinions are those of the authors and do not reflect the position of the Zillow Group. Specifically, we focus on ten metropolitan statistical areas (MSAs) in the United States (Baltimore, Chicago, Cleveland, Los Angeles, Minneapolis/St. Paul, New York City, Philadelphia, Pittsburgh, St. Louis, and Tampa) and use a hedonic pricing approach to identify changes in the implicit prices of yard space and park proximity. We use a combination of data sources, including the Zillow Transaction and Assessment Database (ZTRAX), Open Street Maps, Longitudinal Employer Household Dynamics Origin–Destination Employment Statistics (LODES), National Land Cover Database (NLCD), and the Environmental Protection Agency’s EnviroAtlas to study the interactions between the exposure of a given neighborhood to WFH shock and private yard space, as well as proximity to the nearest park. Our findings suggest that home buyers in all the MSAs, except Minneapolis and Pittsburgh, assigned a greater value to private green amenities during the post-COVID period. However, for the MSAs of Los Angeles, New York, Philadelphia, and Pittsburgh, the marginal willingness to pay (MWTP) for private green spaces decreased in the post-COVID period in the areas with large WFH shocks. No significant change in the MWTP for proximity to public green spaces is noted in the post-COVID period for most MSAs. An event study of yard space reveals that most MSAs experienced an increase in the hedonic price of yard space in the post-COVID period. In the pre-COVID period, for many MSAs, the hedonic price of yard space was decreasing over the years, and this trend reversed in the post-COVID period. These results suggest that the preferred amenity bundles of people living in major cities in the U.S. have shifted as a result of changes in their commutes and work habits.

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%.

GODDAG: Generating Origin-Destination Flow for New Cities Via Domain Adversarial Training

Origin-destination (OD) flow data, which reflects population mobility patterns in the city, is very important in many urban applications, such as urban planning and public resource allocation, etc. However, due to the high cost of money and time during device deployment and social surveys, it is challenging to obtain OD flow data, especially in developing cities and emerging cities where historical OD flow data is scarce. Therefore, it is necessary to investigate a method that can generate OD flow in cities where OD flow data are not available. The research on modeling population mobility in the city has a long history. Traditional gravity models, etc., are too simple to model the complex population mobility; recently proposed machine learning models and deep learning models are not applicable in cities where data are scarce because the parameters must be fitted with abundant data. To solve the problem of difficult access to OD flow data, we propose a method to learn mobility knowledge with ample data in the source city and generate OD flow data in new cities named <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GODDAG</b> ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>G</u></b> enerating <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>O</u></b> rigin- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>D</u></b> estination Flow via <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>D</u></b> omain <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>A</u></b> dversarial Trainin <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>g</u></b> ). Our proposed method consists of two parts, one is a GNN (graph neural networks) based mobility model generating OD flow between every two regions based on regional attributes such as census and POI distribution, and the other is a domain adversarial training strategy to make the model have better transfer ability between different cities. Extensive experiments are conducted on two real-world datasets to prove the validity of our methods.

Back and Forth—On Automatic Exposure of Origin and Dissemination of Files on Windows

The number of Child Sexual Abuse Material (CSAM) cases has increased dramatically in recent years. This leads to the need to automate various steps in digital forensic processing, especially for CSAM investigations. For instance, if CSAM pictures are found on a device, the investigator aim at finding traces about the origin and possible further dissemination, respectively. In this article, we address this challenge with respect to the widespread Windows operating system. We model different common scenarios of system use by CSAM offenders in the scope of file inbound and outbound transfer channels. This gives us insights about digital traces in the Windows operating system and its applications to get knowledge about origin and possible destination of a file. We review available concepts and applications to support this issue. Furthermore, we develop a recursive-based approach and provide a prototype as plugin for the open source application Autopsy. We call our prototype AutoTrack . Our evaluation against the different models of Windows system usage reveals that Autotrack is superior to existing solutions and provides support for an investigator to find digital traces about the origin and possible further dissemination of files. We publish our AutoTrack plugin and thus provide full reproducibility of our approach.