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Searching for an available parking space is a major cause of traffic congestion and pollution. Besides, getting a free spot constitutes a primary worry for drivers on the road, which results in their daily discomfort and stress. Therefore, well-designed parking management tools are needed to effectively allocate parking spaces to drivers, especially in dense urban areas. In this perspective, we propose two linear integer programming models for assigning parking spaces to all road users (private vehicles and carriers) while maximizing parking occupancy. The first combinatorial problem attributes to each driver only one parking space while the second one assigns two parking spaces if no space is suitable for that motorist. Feasible solutions are computed in an exact way by Improved primal simplex method and in an approximated manner by two metaheuristics (Genetic algorithm and Taboo search) for larger scale applications. However, parking problems still persist when parking requests are not properly balanced between a city's zones, a local gridlock problem arises where certain areas are over-used and others under-used. For this reason, we first redistribute parking demands over urban areas before dedicating one or two spaces to each driver. To do this, we consider both solicitation rates in these areas and coverage ratios of received parking demands. Our parking space allocation solution is just one component of a transportation system heavily backed by information and communication technologies. This system consists of effectively managing a city's parking spaces by predicting and understanding their occupancy patterns. The suggested approach is applied to Casablanca city with several parking scenarios in order to evaluate its effectiveness and test its ability to minimize total walking distance, parking costs as well as unmet demand percentages. Experimental results show that our mathematical models can effectively rule out parking problems. The related published methods satisfy at most 75% of received parking requests. However, with our proposal, it is possible to find parking spaces for 87.3% of drivers without any noticeable effort, even an increase of 16.4%. The ordinary walking distance is reduced by 40% if our solution is adopted. Yet, other research works report a decrease of this distance not exceeding 30%. The increase in parking demand satisfaction rates demonstrates the effectiveness of our system, especially in the eyes of urban stakeholders. The reduction in walking distance encourages drivers to use our solution and consequently increases their loyalty.

Simulation and computer modeling have a key role in understanding transportation systems. Focusing on the main system, real-time retrieval of outputs based on mutual interactions of the whole autonomous entities makes the agent-based simulation very promising. This paper deals with an agent-based simulation to investigate and evaluate the potential impacts of implementing Shared Public Transports (SPT) in urban areas. Such a system is intended to integrate the two flows of passengers and containerized freights in Public Transportation (PT) patterns towards more sustainable, efficient, and socially suitable mobility. The proposed model is coupled with a stochastic process in order to provide a range of real-world data of Casablanca city (Morocco) based on institutional surveys. In this respect, two urban transportation systems of freight are tested: (1) the conventional transportation system, (2) the SPT system with heterogeneous fleets. In an effort to sustain efficient and safe movements, this paper examines SPT performances according to a set of key evaluation metrics. Results show that PT stopping remains the most relevant factor when evaluating metrics of the number of waiting containers and waiting time of demand by rates of 82.440% and 62.580%, respectively. Such a waiting containers metric is significantly affected by the volume of demand to transport per time slot by a rate of 78.140%. Under SPT, traffic congestion is the main factor to consider in managing PT with a rate of 65.690% in order to reduce potential accidents. However, demand volume could increase the on-street illegal parking metric by 90.070%. More details are provided below.

The location problem of Bundling Hubs (BHs) remains a contentious issue for efficient shared transportation systems. In this respect, the strategic configuration of BHs plays a crucial role in saving supply costs, covering demand, and minimizing the external effects of Shared Passenger and Freight Public Transportation (SPFPT). As urban areas become crowded, they show a significant increase in congestion and transport demand. Thus, sites where logistic operations, sales, or services are likely to occur, imply the final customers whose transport demand is a key factor that could affect cargo distribution using SPFPT systems. Since each BH should help efficiently to satisfy the transport demand of allocated customers, they would not play their key role if such factor of demand is not involved upstream in the long-term scheduling horizon. This paper focuses on locating BHs, using a Hybrid Robust Machine Learning-Heuristic Algorithm (HR-MLHA), among established ones and existing demand nodes while assessing a dynamic process so that the configured BH network is robust. The feature of robustness is supported by a robust command to keep BHs attractive and demand-responsive for the long-term in dynamic environments, i.e., the cities. To reduce complexity, the conceptual and computational approaches are structured in two main axes. The first axis includes a machine-learning-based zoning approach that helps with targeting the implementation area and assessing demand behavior. The second axis presents a mathematical model of the capacitated two-echelon BH location problem. When looking across the two-echelon location process, we aim at conducting dynamic location analysis using both current and predicted demand. In order to validate our approach, a set of benchmarks has been performed comparing with existing heuristics and using a whole package of experimental and real-life instances. The experimental results provided through the proposed approach have allowed valuable insights into successfully implementing our methodology.

Freight parking is a serious problem in smart mobility and we address it in an innovative manner. We propose an intelligent urban parking management system capable to modify in real time the status of any parking spaces, from a conventional place to a delivery bay and inversely. Its driving force is the parking availability prediction. In this article, we model the parking occupancy by many regression types. We analyze their results, identify their strengths as well as their weaknesses and deduce the most effective one. We calculate the probability of a place being left free by the actuarial method. To do so, we exploit a massive amount of real-time parking availability data collected and disseminated by the City of Melbourne, Australia.

An adequate dimensioning of delivery bays combined with their effective availability facilitates deliverers' tasks, fluidizes urban traffic and improves living conditions in the city. Despite this marked interest, there is still no formal tool associated with a global approach to design and manage delivery areas network. In this paper, we lay the groundwork for a new vision of urban parking. It consists in sharing, equitably between the different road users, all the parking infrastructures. It considers that parking spaces can switch, in real time, their profile from a classic parking place to a delivery bay and vice versa. It is based mainly on the parking availability prediction. We employ three types of ensemble regressors to forecast the number of free parking slots. We examine their outcomes, evaluate their performance and determine their relative advantages as well as disadvantages. We conduct these tests leveraging parking data collected and published by the City of Melbourne, Australia.

Private freight transportation has many advantages, including speed, comfort and affordability. However, it is not often efficient to shorten longer commute times, as it is unable to keep-up with logistics demand during peak hours. Integrative policies, in flow terms, could fill this gap when supported through freight consolidation. Yet, a key issue that emerges in the implementation of consolidation-based transportation systems is that of logistics demand for reducing uncertainties about whether it will be met. The main target of this article is to include demand volatility to carry out a longterm urban land splitting that serve in building consolidation network. To that end, we propose a hybrid machine-learning framework combining several algorithms that are thought robust according to the literature and the achieved accuracy benchmarks. Based upon simulated system, some analytical outlooks are provided below.