Zahra Ghandeharioun


Last Name

Ghandeharioun

First Name

Zahra

ORCID

0000-0003-1500-5077

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Publications 1 - 10 of 14
  • Link travel time estimation with spatial correlations based on OD data
    Item type: Conference Paper
    Ghandeharioun, Zahra; Rau, Moritz; Kouvelas, Anastasios (2021)
    Understanding complicated city traffic patterns has been recognized as a key goal by twenty- first century urban planners and traffic management systems, resulting in a significant rise in the quantity and variety of traffic data gathered. For example, taxi firms in a growing number of large cities have begun to collect metadata for each individual vehicle trip, such as origin, destination, and travel duration. Taxi data offer information on traffic patterns, allowing the study of urban flow – what will traffic look like between two sites at a certain day and time in the future? In this paper, we propose a method based on sparse GPS probe data that focuses on how to allocate travel time data to the different links traveled between GPS observations. This model incorporates the spatial correlations between the links in a network. The main goal of this work is to show, how with a simple adjustment in previously known parametric methods we can consider spatial correlations and improve our results in a more realistic way. For estimation of arterial travel time, the methodology is applied to a case study for the partial network of New York City based on the data, collected from the taxicabs in New York City, providing the locations of origins, destinations and travel times. The model estimates quarter hourly averages of urban link travel times using OD trip data. This study proposes a more accurate approach for estimating link travel times that fully utilizes the partial information received from taxis data in cities.
  • Multi-modal management actions for public transport disruptions
    Item type: Conference Paper
    Siegrist, Mahsa; Ghandeharioun, Zahra; Kouvelas, Anastasios; et al. (2021)
    2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)
    In a public transport disruption, management of the disruption plays a significant role in reducing passengers’ inconvenience. This research aims to evaluate how the delay that passengers experience in a public transport disruption can be reduced, using multi-modal management actions. We consider various traffic management actions implemented in real-time to mitigate the downsides of a disruption, including route and mode adjustments, and capacity increase. Transport operators can implement disruption management actions, such as assigning more vehicle capacity/frequency to the running services; and can inform passengers about the disruption. The benefits of providing updated information to mitigate the downsides of the disruption are large, and could possibly lead to a better mode and route choice. The simulation of the impacts and dynamics of a real-life disruption in our study is performed by an agent-based framework. We demonstrate how much delay could be avoided by implementing multi-modal management actions and analyzing the extent to which they change passengers’ travel behavior, in terms of travel mode and travel time. Results indicate that employing multi-modal management can lead to a reduction of 18% in the delay of affected passengers, when information and capacity management are used together.
  • Online fleet management for on-demand capacitated ride sharing problems
    Item type: Conference Paper
    Ghandeharioun, Zahra; Kouvelas, Anastasios (2019)
    Urban mobility is facing a paradigm shift towards providing more convenient, environmentally friendly and on-demand services. Satisfying customer needs in a cost-efficient way has been the goal of many ride-sharing systems. Taxi ride sharing is considered nowadays an effective service for reducing traffic congestion and pollution; however, the operational strategies that can be used to optimize on-demand ride sharing have not been well investigated. Moreover, only a few studies in the literature provide reliable insights about capacitated ride-sharing systems. In the current work, we focus on solving the on-demand ride sharing service in a real-time frame-work, considering different optimization techniques. Furthermore, by investigating different decision variables and cost functions, we evaluate various management strategies. Furthermore,we study the sensitivity of the solutions to different ride sharing capacities. In this framework, we develop an event-based simulation engine that can be used in order to propose a real-time taxi ride sharing search algorithm. The aim of the algorithm is to quickly decide between competing taxi candidates that satisfy both the user inquiries and the problem constraints. This simulation engine can provide valuable insights regarding different cost functions and parameters variations. Moreover, by utilizing millions of real trip data from the New York City taxi database, we evaluate the feasibility of the proposed framework and evaluate the results for different strategies and optimization techniques.
  • Providing real-time operational solutions for the on-demand capacitated ride sharing problem
    Item type: Conference Paper
    Ghandeharioun, Zahra; Kouvelas, Anastasios (2019)
  • Travel time estimation for urban arterials based on origin-destination data and spatial correlations
    Item type: Other Conference Item
    Ghandeharioun, Zahra; Rau, Moritz; Kouvelas, Anastasios (2022)
    2022 TRB Annual Meeting Online Program Archive
  • Optimization of shared on-demand transportation
    Item type: Doctoral Thesis
    Ghandeharioun, Zahra (2024)
    Nations worldwide are experiencing significant urban growth, with over half of the global population currently residing in cities. This urbanization has increased urban commuting, leading to issues like congestion, air and noise pollution, and threats to public health. Recent years have witnessed a transformation in transportation driven by information technology, introducing innovative mobility solutions to tackle urban mobility challenges. These innovations encompass on-demand and shared mobility services, enhancing transportation efficiency and convenience. The integration of these solutions with public transit holds the potential to revolutionize the entire transportation system. Overcoming these challenges requires a comprehensive evaluation of the advantages and disadvantages of shared mobility services, ensuring a shift toward sustainable mobility in the future. This thesis aims to explore the optimization of on-demand transportation services in urban areas by employing methods in three aspects. The first part of this thesis analyzes historical travel time data from on-demand transport services, like taxis, to gain insights into traffic patterns and estimate arterial travel time precisely. It introduces a novel methodology that uses sparse GPS probe data and considers spatial correlations between network links. This research demonstrates the improved accuracy of travel time estimation by factoring in progressive spatial correlations. A case study in a partial network of New York City, using taxi data, shows enhanced travel time estimation accuracy, benefiting urban traffic optimization and congestion identification. The second part of this thesis centers on optimizing on-demand services with a real-time shuttle ridesharing algorithm. This novel algorithm efficiently matches ride requests to a fleet of vehicles, using a flexible simulation framework that adapts to different scenarios and incorporates real-time traffic data. By focusing on fleet capacities and tolerance times, the study shows that a reduced number of high-capacity taxis, along with optimized operational policies, significantly reduces waiting times and in-car delays for Manhattan taxi rides. The final part of this thesis focuses on developing precise short-term demand forecasting models for on-demand services, with an emphasis on deep learning techniques. It seeks to enhance prediction accuracy, investigate data granularity's impact, explore temporal and spatiotemporal variables, compare the model's performance with traditional and complex machine learning methods, and highlight the benefits of spatiotemporal considerations and vector embedding for improved prediction accuracy. The research presented in this thesis offers valuable implications for both research and practical applications. First, accurate estimates and predictions of travel times for urban links are crucial for optimizing urban traffic operations and identifying traffic congestion points. Providing precise travel time information offers benefits to users and operators by enabling them to choose better paths within the network and reduce overall travel time. Second, the potential of ridesharing services, optimized in real-time with dynamic traffic data, is shown by the proposed modular framework, together with the novel matching algorithm in Chapter 3 . The importance of system parameters and tailored operational policies to improve urban transportation systems gives valuable insight for the design of such services for operators. Moreover, the precise demand prediction can help the operators plan the fleet dispatching more efficiently.
  • Link travel time estimation for arterial networks based on sparse GPS data and considering progressive correlations
    Item type: Journal Article
    Ghandeharioun, Zahra; Kouvelas, Anastasios (2022)
    IEEE Open Journal of Intelligent Transportation Systems
    Understanding complicated city traffic patterns has been recognized as a critical goal by twenty-first-century urban planners and traffic management systems, resulting in a significant rise in the quantity and variety of traffic data gathered. For example, in a growing number of large cities, taxi firms have begun collecting metadata for each vehicle trip, such as origin, destination, and travel duration. Taxi data offer information on traffic patterns, allowing the study of urban flow—what will traffic look like between two sites on a particular day and time in the future? This paper proposes a method based on sparse GPS probe data, that focuses on allocating travel time data to the different links traveled between GPS observations. This model incorporates the progressive spatial correlations between the links in a network. The main goal of this work is to show how we can consider progressive spatial correlations and improve our results more realistically with a simple adjustment in the previously known parametric methods. For estimating arterial travel time, the methodology is applied to a case study for the partial network of New York City-based on the data collected from the taxicabs in New York City, providing the locations of origins, destinations, and travel times. The model estimates quarter-hourly averages of urban link travel times using OD trip data. This study proposes a more accurate approach for estimating link travel times, that fully utilizes the partial information received from taxi data in cities.
  • Online fleet management operations for on demand capacitated ride sharing systems
    Item type: Conference Poster
    Ghandeharioun, Zahra; Kouvelas, Anastasios (2020)
  • Real-time ridesharing operations for on-demand capacitated systems considering dynamic travel time information
    Item type: Journal Article
    Ghandeharioun, Zahra; Kouvelas, Anastasios (2023)
    Transportation Research Part C: Emerging Technologies
    Urban mobility is facing a paradigm shift towards providing more convenient, environmentally friendly, and on-demand services. Satisfying customer needs in a cost-efficient way has been the goal of many ridesharing systems. Capacitated ridesharing is considered as an effective service for reducing traffic congestion and pollution nowadays. Providing more operational strategies that can optimize on-demand ridesharing needs further investigation. In the current work, we focus on developing a matching algorithm for solving the on-demand ridesharing operation task in a real-time setting. We develop a simulation framework that can be used to propose a real-time shuttle ridesharing search algorithm. We propose a novel, computationally efficient, real-time ridesharing algorithm. We formulate the ridesharing assignment algorithm as a combinatorial optimization problem. The computational complexity of the proposed algorithm is reduced from exponential to linear, and the search space of the optimization problem is reduced by introducing heuristics. Our approach implements dynamic congestion by regularly updating the network’s road segments’ travel time during the simulation horizon to have more realistic results. We demonstrate how our algorithm, when applied to the New York City taxi dataset, provides a clear advantage over the current taxi fleet in terms of service rate. Furthermore, the developed simulation framework can provide valuable insights regarding cost functions and operational policies.
  • Exploring Deep Learning Approaches for Short-Term Passenger Demand Prediction
    Item type: Journal Article
    Ghandeharioun, Zahra; Zendehdel Nobari, Parham; Wu, Wenhui (2023)
    Data Science for Transportation
    An accurate short-term passenger demand forecast makes a contribution to the coordination of traffic supply and demand. Forecasting the short-term passenger demand for the on-demand transportation service platform is of utmost significance since it might incentivize empty cars to relocate from over-supply regions to over-demand regions. Yet, because spatial, temporal, and exogenous dependencies need to be evaluated concurrently, short-term passenger demand forecasting may be rather difficult. This article aims to investigate several methods that can be utilized to forecast short-term traffic demand, with a primary emphasis on deep learning approaches. We examine varying degrees of temporal aggregation and how these levels affect various architectural configurations. In addition, by analyzing 22 models representing 5 distinct architectural configurations, we illustrate the influence of varying layer configurations within each architecture. The findings indicate that the long-term short memory (LSTM) structures perform the best for short-term time series forecasting, but more complex architectures do not significantly enhance the outcomes. Moreover, considering the spatiotemporal aspects results in an improvement in the prediction of more than fifty percent. In addition, we investigate the vectorization of time, also known as Time2Vec, as a way of embedding to make it possible for a selected algorithm to recognize periodic characteristics in time series, and we show that the outcome is improved by fifteen percent.
Publications 1 - 10 of 14