Origin Destination Research Articles

Schedule-based transit assignment with online bus arrival information

Most public transportation services deviate from their published schedule. To cope with the delay caused by the unreliable service, passengers use online information about the bus arrival time which affects their route choice behavior. Current schedule-based transit assignment models fail to capture the passengers’ adaptive response to unreliable service, resulting in an inaccurate estimation of passenger wait time and passenger loads on various transit routes. The current study proposes schedule-based transit assignment models that incorporate online bus arrival information when modeling the passenger route choice in a stochastic and time-dependent transit network. The authors propose that passengers employ strategies when traveling between different origin–destination pairs not only due to the limited capacity of vehicles but also to cope with the transit delay. The passenger routing problem is modeled as a Markov Decision Process, and efficient algorithms are developed to solve this problem. Depending on the vehicle capacity, two types of assignment models are presented, namely, uncapacitated and capacitated assignments. When penalties for arriving at the destination outside the desired arrival time window are not applied, the uncapacitated assignment problem is formulated as a linear program. On the other hand, the capacitated assignment is formulated as a variational inequality problem for which an efficient Method of Successive Averages-based heuristic solution algorithm is proposed. Computational experiments are presented for a small and a large schedule-based transit network. The results show that denied boarding in an unreliable network leads to higher expected costs to passengers compared to the reliable and uncongested network. Furthermore, the analysis shows that the strategies evaluated with reliable schedule assumption lead to unreliable paths in the network and produces more transferring flow than should happen in practice. The application of our method to a subnetwork of the Twin Cities transit network with artificial demand reveals that passengers traveling from a residential area to the University of Minnesota campus may prefer taking a path with transfer in the event of highly unreliable transit service on the direct routes.

Freight trip distribution using spatiotemporal aggregate data: A modified collective flow diffusion model-based approach

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.

Iterative Backpropagation Method for Efficient Gradient Estimation in Bilevel Network Equilibrium Optimization Problems

Network optimization or network design with an embedded traffic assignment (TA) to model user equilibrium principle, sometimes expressed as bilevel problems or mathematical programs with equilibrium constraints (MPEC), is at the heart of transportation planning and operations. For applications to large-scale multimodal networks with high dimensional decision variables, the problem is nontrivial, to say the least. General-purpose algorithms and problem-specific bilevel formulations have been proposed in the past to solve small problems for demonstration purposes. Research gap, however, exists in developing efficient solution methods for large-scale problems in both static and dynamic contexts. This paper proposes an efficient gradient estimation method called Iterative Backpropagation (IB) for network optimization problems with an embedded static TA model. IB exploits the iterative structure of the TA solution procedure and simultaneously calculates the gradients while the TA process converges. IB does not require any additional function evaluation and consequently scales very well with higher dimensions. We apply the proposed approach to origin-destination (OD) estimation, an MPEC problem, of the Hong Kong multimodal network with 49,806 decision variables, 8,797 nodes, 18,207 links, 2,684 transit routes, and 165,509 OD pairs. The calibrated model performs well in matching the link counts. Specifically, the IB-gradient based optimization technique reduces the link volume squared error by 98%, mean absolute percentage error (MAPE) from 95.29% to 21.23%, and the average GEH statistics from 24.18 to 6.09 compared with the noncalibrated case. The framework, even though applied to OD estimation in this paper, is applicable to a wide variety of optimization problems with an embedded TA model, opening up an efficient way to solve large-scale MPEC or bilevel problems. Funding: The study is supported by IVADO Postdoctoral Fellowship scheme 2021, HSBC 150th Anniversary Charity Programme HKBF17RG01, National Science Foundation of China (71890970, 71890974), General Research Fund (16212819, 16207920) of the HKSAR Government, and the Hong Kong PhD Fellowship.

A hybrid modelling framework for the estimation of dynamic origin–destination flows

The dynamic origin–destination flow estimation (DODE) problem requires scalable methods for large scale traffic networks and consistent techniques for capturing both uncongested and congested traffic conditions. Despite numerous efforts on incorporating multifold data sources and developing manifold mathematical models, the DODE problem remains a challenging problem in terms of both scalability and consistency. To fill this gap, we propose a novel hybrid DODE framework that integrates region-level (macro) and centroid-level (micro) traffic dynamics. The region-level traffic flows are described by the macroscopic fundamental diagram, while the centroid-level traffic flows are represented by the linear mapping of origin–destination flows onto link counts. This hybrid approach enables us to (i) incorporate region-level traffic measures into the problem, addressing scalability issues arising in large scale traffic networks, and (ii) capture non-linear behaviour of traffic in the regional context, enhancing consistency of the estimation results with respect to traffic conditions. The proposed methodology is experimented in a large-scale traffic network, which is benchmarked for DODE problems. The results indicate an outstanding performance of the hybrid DODE particularly in congested traffic conditions and highlight the effectiveness of aggregated (regional) traffic models in enhancing DODE methods with minimal computational burden.

Short-Term Prediction of Time-Varying Passenger Flow for Intercity High-Speed Railways: A Neural Network Model Based on Multi-Source Data

The accurate prediction of passenger flow is crucial in improving the quality of the service of intercity high-speed railways. At present, there are a few studies on such predictions for railway origin–destination (O-D) pairs, and usually only a single factor is considered, yielding a low prediction accuracy. In this paper, we propose a neural network model based on multi-source data (NN-MSD) to predict the O-D passenger flow of intercity high-speed railways at different times in one day in the short term, considering the factors of time, space, and weather. Firstly, the factors that influence time-varying passenger flow are analyzed based on multi-source data. The cyclical characteristics, spatial and temporal fusion characteristics, and weather characteristics are extracted. Secondly, a neural network model including three modules is designed based on the characteristics. A fully connected network (FCN) model is used in the first module to process the classification data. A bi-directional Long Short-Term Memory (Bi-LSTM) model is used in the second module to process the time series data. The results of the first module and the second module are spliced and fused in the third module using an FCN model. Finally, an experimental analysis is performed for the Guangzhou–Zhuhai intercity high-speed railway in China, in which three groups of comparison experiments are designed. The results show that the proposed NN-MSD model can predict many O-D pairs with a high and stable accuracy, which outperforms the baseline models, and multi-source data are very helpful in improving the prediction accuracy.

An approach toward correct policy based on mass transit lines' demand analysis

When a BRT line overlaps with the metro lines, it is sometimes believed that the Bus Rapid Transit (BRT) line should be removed, and passengers can use the rail lines instead. The tendency to remove BRT lines is because of the BRT system's negative factors, such as street occupation, political pressures, and local businesses' protests (due to the removal of curbside parking) against the BRT. In this study, the coverage capability of BRT by metro was evaluated. In addition, an analysis of the origin–destination potential attraction was also performed. The station overlap analysis results of this study contradict the common belief that the rail system is preferable for passengers. Station overlap analysis checks if the BRT station accessibility radius is potentially compensated by metro accessibility radius overlap. In addition, an analysis of the origin–destination potential attraction is also performed. This analysis aims to assess the potential of a metro line in attracting existing demand for BRT line segments that are candidates for removal. The methodology proposed here is applied to BRT Line 800 in Mashhad, Iran. The data was obtained from Mashhad entry-only smart card transactions. While initially it was supposed that the BRT line segment parallels to Metro Line 2 is useless, the results of the station overlap analysis showed that metro stations could cover only 31% of the BRT stations. Also, the origin–destination potential attraction analysis showed that only 20% of BRT trips could be shifted to the metro line. The BRT is more consistent with sustainable transportation principles than the metro. Our research also shows that people prefer the BRT more because of its station's proximity and coverage, which underground modes cannot replace it. This contradicts the common belief that the rail system is preferable by users.

Acculturation and resilience of immigrant-origin youth: Do their school experiences reflect nonimmigrants' "native supremacy"?

The successful integration of immigrant-origin youth is a highly important issue for multiple stakeholders in many countries. It has important benefits both to countries of destination and countries of origin, as well as to immigrants and nonimmigrants. In this article, I examine immigrant-youth adaptation through the lens of a recently developed resilience model integrating acculturation and social psychological influences on adaptation. Who among immigrant-origin youth adapt well, academically, and socially, in the Greek school context? What is the role of acculturation in immigrant youth resilience? These questions are addressed using scientific evidence drawn from the Athena Studies of Resilient Adaptation (AStRA) project, a three-cohort, three-wave longitudinal project on immigrant-origin youth adaptation conducted in Greece, as well as from the international literature. Following an anti-racist research approach to understanding the AstRA findings, I will argue that the lived school experiences of immigrant-origin youth may be a reflection of societal-level xenophobic and anti-immigrant attitudes. Such systemic and structural racism is the key determinant of the difficulties they face in their adaptation. The findings presented reveal the need to promote an equitable and inclusive education that will be beneficial for all students promoting their well-being, and their sense of belonging to school and society.

Data-Driven Deployment of Cargo Drones: A U.S. Case Study Identifying Key Markets and Routes

Electric and autonomous aircraft (EAA) are set to disrupt current cargo-shipping models. To maximize the benefits of this technology, investors and logistics managers need information on target commodities, service location establishment, and the distribution of origin–destination pairs within EAA’s range limitations. This research introduces a three-phase data-mining and geographic information system (GIS) algorithm to support data-driven decision-making under uncertainty. Analysts can modify and expand this workflow to scrutinize origin–destination commodity flow datasets representing various locations. The algorithm identifies four commodity categories contributing to more than one-third of the value transported by aircraft across the contiguous United States, yet only 5% of the weight. The workflow highlights 8 out of 129 regional locations that moved more than 20% of the weight of those four commodity categories. A distance band of 400 miles among these eight locations accounts for more than 80% of the transported weight. This study addresses a literature gap, identifying opportunities for supply chain redesign using EAA. The presented methodology can guide planners and investors in identifying prime target markets for emerging EAA technologies using regional datasets.

Network Tomography with Partial Topology Knowledge and Dynamic Routing

Networks are always evolving to meet the needs of progressing and novel applications. To this end, improved network capacity, latency and security are required. Monitoring is key to achieving these objectives. It is a cornerstone for the uninterrupted network operation and the applications’ QoS management. Network tomography uses a subset of monitoring information, corresponding to partial view of the network state, to estimate wide-sense network performance metrics. In this paper, we present a novel machine learning (ML) formulation for Network Tomography. It is novel in that its features are designed under the assumption that: (i) the existence of certain links of the network is not known (e.g., due to security reasons), (ii) the routing is dynamic (non-deterministic), i.e., for the same origin–destination node pair, a different route may be selected depending on the state of certain links. These assumptions are typically present in modern networks. The formulation can be used to estimate both additive and non-additive performance metrics. We evaluate our proposal using different ML algorithms: neural networks (NNs), Gaussian regression and linear regression with interactions. Our simulations indicate that our ML formulation has better estimation accuracy compared to traditional algebraic or other ML approaches that cannot or do not take into account these two hypotheses. Regarding the accuracy of the examined ML algorithms, the differences mainly depend on the additive or not nature of the network metrics.

Deep Learning for Metro Short-Term Origin-Destination Passenger Flow Forecasting Considering Section Capacity Utilization Ratio

Origin-destination (OD) short-term passenger flow forecasting (OD STPFF) in urban rail transit (URT) is essential for developing timely network measures. The capacity utilization ratios of critical sections are key factors in developing these measures. The OD pairs passing through critical sections require a higher prediction accuracy than others; however, most studies have raised equal concerns on the prediction accuracy of each OD pair, namely, prediction at the network level. To address this problem, we raise heterogeneous time-variant concerns on OD pairs and employ an operation-oriented deep-learning architecture called the spatiotemporal convolutional neural network (STCNN) for realizing short-term OD passenger flow prediction. The architecture contains OD pair importance calculation, lagged spatiotemporal relationship construction, lagged spatiotemporal learning, real-time information learning, and sequential-temporal learning blocks. To this end, critical OD pairs are ascertained first, and the topological lagged spatiotemporal relationship among critical OD pairs are constructed and then normalized into grid-shaped data. The third block utilizes a convolutional neural network (CNN) to learn the grid-shaped lagged spatiotemporal feature and real-time information in parallel. A temporal convolutional neural network (TCN) is utilized for learning the sequential-temporal feature at last. Further, we design a time-varying weighted masked loss function to jointly reinforce the concerns on critical OD pairs during model training. Finally, we test the proposed STCNN and its components on a field dataset from Chengdu Metro. Although the proposed STCNN performs only slightly better at the network level than the other models, it outperforms state-of-the-art methods with significant superiority on critical OD pairs.

Spatial–Temporal Analysis of Vehicle Routing Problem from Online Car-Hailing Trajectories

With the advent of the information age and rapid population growth, the urban transportation environment is deteriorating. Travel-route planning is a key issue in modern sustainable transportation systems. When conducting route planning, identifying the spatiotemporal disparities between planned routes and the routes chosen by actual drivers, as well as their underlying reasons, is an important method for optimizing route planning. In this study, we explore the spatial–temporal differences between planned routes and actual routes by studying the popular roads which are avoided by drivers (denoted as: PRAD) from car-hailing trajectories. By applying an improved Hidden Markov Model (HMM) map-matching algorithm to the original trajectories, we obtain the Origin-Destination (OD) matrix of vehicle travel and its corresponding actual routes, as well as the planned routes generated by the A* routing algorithm. We utilize the Jaccard index to quantify the similarity between actual and planned routes for the same OD pairs. The causes of PRADs are detected and further analyzed from the perspective of traffic conditions. By analyzing ride-hailing trajectories provided by DiDi, we examine the route behavior of drivers in Wuhan city on weekdays and weekends and discuss the relationship between traffic conditions and PRADs. The results indicate that the average accuracy of GNSS trajectory point-to-road map-matching reaches 88.83%, which is approximately 12% higher than the accuracy achieved by the HMM map-matching method proposed by Hu et al. Furthermore, the analysis of PRAD causes reveals that PRADs occurring on weekdays account for approximately 65% and are significantly associated with traffic congestion and accidents during that time. The findings of this study provide insights for future research on sustainable transportation systems and contribute to the development of improved route-planning strategies.

Social class, intergenerational mobility, and desired number of children in China

With more countries entering an era of low fertility, factors that may shape individuals' childbearing desires have been extensively explored. One's intergenerational social mobility experience, however, remains under-discussed. This paper examines this issue against the backdrop of China, in which the total fertility rate has rapidly declined to the lowest-low level in recent decades. Using pooled five-wave data from the Chinese General Social Survey from 2010 to 2017, we found a checkmark-shaped relationship between social class and fertility desire. Chinese farmers have the greatest desired number of children, manual workers have the lowest, and the two upper classes lie in the middle. In general, one's origin class and destination class play a similar role in shaping fertility desire, demonstrating the coexistence of socialization and acculturation processes. The gender-specific examination further suggests that, compared to women, men are more reluctant to change the fertility attitude learned from the origin class. Based on these findings, our study proposes a novel explanation of the decline in fertility desire in China and contributes to the literature on how social mobility experiences and gender ideology jointly construct fertility desire.

Workplace Assignment to Workers in Synthetic Populations in Japan

In this article, we assign workplace attributes to each worker in each household in a synthetic population using multiple censuses conducted in Japan. The synthetic population is a set of artificial individual attributes for each resident that is synthesized according to census data. We have synthesized a set of the synthetic populations of Japan. We assign a workplace attribute to each worker to estimate daytime population distribution and develop activity-based models in agent-based or microsimulations. Although statistical information in a residential area or a working place is released by the government and some individual moving data are released by cellphone companies, it is hard to collect the information with home and workplace location of a worker with their family and working information. We employ origin–destination–industry (ODI) statistics to estimate workplaces for workers. Since some attributes in ODI statistics are not available for privacy reasons, we propose a workplace assignment method for all cities, towns, and villages using restricted ODI and OD statistics in Japan. We show how much difference there are between the number of workers using the complete ODI statistics and the number of workers by the proposed workplace assignment method. We show that 88.2% of workers in a city in Japan are assigned to correct cities as workplaces by our proposed method. We also show several maps of daytime population distributions by our proposed method. Synthetic populations with workplace attributes enable real-scale social simulations to design transport or business systems in times of peace or to estimate victims and plan recoveries in times of emergency, such as disasters or pandemics.

How can trade partners be chosen when facing food scandals? China's milk scandal as a natural experiment

AbstractUsing China's milk scandal as a natural experiment, this paper explores whether the countries of origin and export destinations have the same responses to the original country's food scandal. Our difference‐in‐difference estimation shows that the outbreak of China's milk scandal had asymmetric impacts on China's total imports (increased by 23.4%) and exports (sharply dropped by 65.8%). The results further show that China's milk scandal contributed to import increases from European and Oceanian countries. Moreover, China's milk scandal worsened exports, mainly those going to neighbouring Asian regions but increased exports to Oceania. A product quality index is constructed to explain this finding. Intuitively, consumers' perceived quality of Chinese products declined, and they tended to consume products from other countries. The lower the product quality was with those in China, the lower the perceived quality and safety, thereby affecting demand. During the scandal, Chinese consumers tended to buy high‐quality dairy products from Europe and Oceania rather than the perceived unreliable dairy products produced by China or neighbouring countries. We conclude that product quality plays a key role in imports and exports when facing food scandals.

Reproducible air passenger demand estimation

The availability of passenger demand estimates for air traffic routes is crucial to a plethora of application and research problems ranging from, e.g., optimization of airline fleet utilization to complex simulations of whole air transport systems. However, somewhat surprisingly, such demand estimates appear hard to come by directly or even to generate by means of published models. This is in large parts due to the widespread use of expensive proprietary data (such as airline-specific ticket prices for certain flight connections) which is typically employed both to calibrate demand estimation models as well as to evaluate such models in order to obtain demand estimates for given origin–destination airport pairs. With this work, we propose building a data set for the European air transport system from given base data and automatically extracted and processed data from external sources, all of which are (made) freely available in the public domain and thus enable reproducibility and facilitate comparability of research involving air passenger transportation. Moreover, we challenge the long-standing tradition of calibrating so-called gravity models for demand estimation by standard linear regression. For the European air transport system, using the aforementioned publicly available data, we demonstrate that machine learning models and techniques like neural networks or the “kernel trick” can significantly improve the estimation quality with respect to ordinary least-squares. In fact, our results—the best of which were obtained using our feed-forward neural network model (four hidden layers with tanh and ReLU activations)—achieve a performance at least comparable to what has been reported in earlier works that utilized non-public data. Computer code to generate air passenger demand estimates is made publicly available online along with our base data and implementation to collect and curate external data.

Development of a Path Tracking System for GPS Denied Environment

Navigation applications such as Google Maps and Waze fail to function at global positioning system (GPS) denied environments. This is typically the primary problem faced by mountain hikers who have lost their way in a dense forest or a dementia patient who has lost his/her way. Navigation applications fail to function at places without internet signal. Also, a dementia patient may not be able to recall his original destination, rendering the GPS useless.The development of a path tracking system which does not rely on GPS is therefore necessary in such a situation. The system should be capable of guiding a person to return back to his/her original starting point without the requirement of a GPS. In this paper, we present the development of a path tracking system which exploits the inertial measurement unit embedded in a mobile phone. Doing so, the use of the GPS can be evaded. The system that we have developed consists of 3 main functions, i.e. (i) path recording, (ii) path navigation, and (iii) map generation. An SQLite database is setup in this application for storing data. Java libraries such as GraphView and Canvas are implemented to perform observation on the data obtained from the sensors and also to map the route according to the data stored into the device. The validation results show that the accuracy of the system is beyond 90%. The results convincingly corroborate the reliability and feasibility of the system.

Adaptative generalisation over a value hierarchy for the [formula omitted]-anonymisation of Origin–Destination matrices

The study of transportation relies on mobility data, containing information on the whereabouts and movements of individuals in a study area. These data are often represented in the simple form of origin–destination (OD)-matrices, which are a valuable indicator for the management of transportation networks but also present risks to the privacy of the individuals. In particular, the significant size (i.e., number of distinct flows) and high number of modalities (produced by a high-resolution zoning) of OD-matrices call for an adapted, fast algorithm that can efficiently anonymise them. In this paper, we develop a lightweight approach for the k-anonymisation of OD-matrices that exploits the low dimension of the data to explore a larger solution space than regular generalisation algorithms, while keeping relevant restrictions of the search space in order to be scalable on matrices with high number of flows. We apply it to a variety of real-world large-scale O-D matrices collected by the New York City Taxi and Limousine Commission and derived from the Data for Development (D4D) challenge organised by Orange in Senegal and Côte d’Ivoire. Compared to an extensive benchmark of regular generalisation algorithms and mobility anonymisation state-of-the-art, we show that our method is 27% more precise and 9 times faster than comparable approaches able to scale on the same datasets.

The impact of COVID-19 pandemic on the fundamental urban mobility theories using transit data from Singapore

The COVID-19 pandemic has caused changes in urban mobility patterns due to the temporary movement restrictive policies imposed by local governments and the long-term hybrid work arrangements adapted by companies. This study re-examines the fundamental theories of human mobility in Singapore at the pre-pandemic, pandemic and endemic stages. In particular, we estimated three well-established mobility models in complexity science: gravity model, radiation model and visitation law for investigating the effects of COVID-19 pandemic in different stages, using the transit Origin-Destination (O-D) data collected in Singapore. Overall, the results show that the three mobility theories remain valid throughout the different periods with visitation law outperforming the gravity and the radiation models. At the pre-pandemic stage, the performance of the gravity and radiation models were comparable. The performance of the gravity model has improved compared to its pre-pandemic performance, showing 6–18% improvement at the pandemic stage and 9–15% improvement at the post pandemic stage, whereas the performance of the radiation model hasn’t changed much, making gravity model a better-performing model than the radiation model for these periods. Our results also show that in the gravity and radiation models, gross floor area is a better predictor for transit flows than the population, whereas in the visitation law use of population variable results in better predictions. Our study is a first attempt in providing a revised understanding of the performance of the fundamental human mobility laws in the post-pandemic world.

Estimation of flow trajectories in a multi-lines transportation network

Characterizing a public transportation network, such as an urban network with multiple lines, requires the origin–destination trip counts during a given period. Yet, if automatic counting makes the embarkment (boarding) and disembarkment (alighting) counts in vehicles known, it often happens that pedestrian transfers between lines are harder to track, and require costly and invasive devices (e.g., facial recognition system) to be estimated. In this contribution, we propose a method, based on maximum entropy and involving an iterative fitting procedure, which estimates the passenger flow between origins and destinations solely based on embarkment and disembarkment counts. Moreover, this method is flexible enough to provide an adaptable framework in case additional data is known, such as attraction poles between certain nodes in the network, or percentages of transferring passengers between some lines. This method is tested on toy examples, as well as with the data of the public transportation network of the city of Lausanne provided by its Transportation Agency (tl), and gives arguably convincing estimations of the transportation flow.

A heuristic approach to the stochastic capacitated single allocation hub location problem with Bernoulli demands

Hubs are critical components of transportation and distribution systems, and hub networks play a special role in freight and passenger transportation services worldwide. This paper studies a capacitated single allocation hub location problem with Bernoulli demands. Since the origin-destination (OD) demands are stochastic in nature, and the nodes are allocated to hubs before knowing their realized values, the actual total demand allocated to each hub is uncertain. Therefore, demand can exceed the capacity of hubs, rendering a need for outsourcing. The problem is studied under two distinct outsourcing policies, namely the facility and customer outsourcing. Mathematical models are developed for each case as two-stage stochastic programs. Deterministic equivalent formulations are obtained for problems, assuming a homogeneous demand distribution for all OD pairs. A Tabu Search-based algorithm is presented as a solution approach to deal with large problem instances. Extensive computational tests demonstrate the outstanding performance of the developed models and the metaheuristic procedure in terms of solution quality and computational time. The relevance of using stochastic programming approach for the problems is demonstrated via computational results. This study also reports solutions for problems with 200 nodes for the first time.