Freight Parking Research Articles

Unraveling intra-urban freight parking patterns: A data-driven geospatial study of shared logistics sector in Hong Kong

Insufficient urban parking in densely populated cities has led to challenges like traffic congestion and unauthorized parking. While existing literature extensively covers parking behaviors among private vehicles and taxis for commuters, limited research has been proposed centering on urban shared freight activities. It can be problematic to neglect shared freight activities because these logistics activities often require more flexible parking space for goods loading/unloading near urban destinations. This study therefore introduces a geospatial data-driven approach to investigate urban parking behaviors from shared freight activities using GPS trajectories collected in Hong Kong. The analysis involves three main folds: using rank-size distribution and log-odds ratio to comparatively examine spatial heterogeneity of parking, evaluating illegal on-street parking events via illegal parking index, and quantifying relationships between parking behaviors and urban functions using random forest (RF) and SHapley Additive exPlanations (SHAP). The findings reveal significant concentrations of parking events on weekdays/weekends and with different parking durations. Particularly, high illegal on-street parking concentration is reported in Tsuen Wan and Kwai Chung, where the port container terminals and industrial activities are located. We further discover that industrial and dining densities both indicate significantly positive impacts on the relationship to the increases of overall parking frequency and illegal parking index. This study can be of great interest to current parking management and enforcement strategies in cities and relevant practical implications are discussed.

Improving freight parking needs in Brazilian cities with city logistics initiatives

The objective of this study is to analyze the demand for loading and unloading parking spaces in the Brazilian cities of São João Dei Rei and Itajubá, in the state of Minas Gerais, using freight trip generation models. The number of employees is used as an independent variable to generate the models. The results showed that the studied regions receive an average of 539 and 709 trips daily. This is problematic since currently available exclusive parking spaces for loading/unloading cover only 18% of the estimated demand in São João Del Rei and 34.5% in Itajubá. Based on these results, this study proposes new locations. It suggests City Logistics measures that could be used in conjunction with the allocation of new parking spots to reduce the demand for loading/unloading spaces.

Cognitive digital twins for freight parking management in last mile delivery under smart cities paradigm

This paper examines the Freight Parking Management Problem (FPMP) of last-mile delivery within the context of Smart Cities where objects are managed by Digital Twins. Specifically, we investigate how Cognitive Digital Twins - Digital Twins with augmented semantic capabilities - can enhance instantly updated knowledge of parking connectivity to optimize logistics operations planning and urban resource allocation. We present a four-layer architectural framework to integrate individual logistics objects and systems into Smart Cities at a semantic level, with underlying enabling technologies and standards including Property Graph, Web Ontology Language (OWL), and Web of Things. Next, we conduct a case study of parcel delivery in Paris using a real-life Digital Twins platform called Thing in the future (Thing’in) by Orange France, coupled with an agent-based simulation model on AnyLogic, to demonstrate a real-world application of our approach. The results suggest that semantics-enabled Digital Twins connectivity can increase the comprehensive understanding of the delivery environment and enhance cooperation between heterogeneous systems, ultimately resulting in improved logistics efficiency, reduced negative externalities, and better utilization of resources. Furthermore, this work showcases potential new business services for logistics service providers and provides managerial insights for city planners and municipal policymakers. An actual mobile application prototype is presented to showcase the applicability of the work.

Uncovering freight curbside management effects on cities’ sustainable development goals. A systematic literature review.

Scarce urban space needs to be wisely managed to avoid cities’ unsustainability. Overlooking freight activities in urban mobility policies leads to conflicting scenarios in public space use with negative consequences in congestion, pollution, crashes, and productivity losses. This paper aims at identifying the unsustainable effects of freight parking practices and the solutions reported in the literature to overcome them. A systematic literature review was conducted to collect quantitative evidence of curbside management impacts on sustainability. Key performance indicators from the 11th Sustainable Development Goal (SDG11) were linked to the 55 selected studies. Findings suggested positive impacts from four practices of freight curbside management and the need to include people-centred approaches in the design, implementation, and evaluation of public space policies.

The tussle for freight parking in seaport cities; the case of Tema

The main entry and exit points for freight into Ghana are by the sea. However, freight is transported to the hinterlands and beyond, by road only. Freight vehicle operators who haul these goods to various destinations encounter several difficulties related to parking at the seaport of Tema; the biggest port in Ghana. This study seeks to understand the characteristics and challenges of freight parking in Tema. A total of 202 freight vehicle operators were randomly selected from the major parking areas within the Tema Metropolis in addition to purposively selected institutions, responsible for ensuring safe and orderly parking in the study area. The study revealed that more than 70% of freight vehicle operators park over 24 hours in undesignated spaces and their activities affect the safety, health, and environmental conditions in the areas around the Tema Port. Thus, the widespread usage of undesignated parking by operators of freight makes these locations accident-prone. Considering that the Tema Harbour is the major entry point of freight into Ghana and adjoining landlocked countries, freight vehicle operators found the port vicinity as a ‘market’ for their activities. The paper recommends the need to formulate a freight parking management framework as an initial intervention to guide subsequent parking interventions.

Enabling Factors and Durations Data Analytics for Dynamic Freight Parking Limits

Freight parking operations occur amid conflicting conditions of public space scarcity, competition with other users, and the inefficient management of loading zones (LZ) at cities’ curbside. The dynamic nature of freight operations, and the static LZ provision and regulation, accentuate these conflicting conditions at specific peak times. This generates supply–demand mismatches of parking infrastructure. These mismatches have motivated the development of Smart LZ that bring together technology, parking infrastructure, and data analytics to allocate space and define dynamic duration limits based on users’ needs. Although the dynamic duration limits unlock the possibility of a responsive LZ management, there is a narrow understanding of factors and analytical tools that support their definition. Therefore, the aim of this paper is twofold. Firstly, to identify factors for enabling dynamic parking durations policies. Secondly, to assess data analytics tools that estimate freight parking durations and LZ occupation levels based on operational and locational features. Semi-structured interviews and focus group analyses showed that public space use assessment, parking demand estimation, enforcement capabilities, and data sharing strategies are the most relevant factors when defining dynamic parking limits. This paper used quantitative models to assess different analytical tools that study LZ occupation and parking durations using tracked freight parking data from the City of Vic (Spain). CatBoost outperformed other machine learning (ML) algorithms and queuing models in estimating LZ occupation and parking durations. This paper contributes to the freight parking field by understanding how data analytics support dynamic parking limits definition, enabling responsive curbside management.

An agent-based simulation assessment of freight parking demand management strategies for large urban freight generators

A growing body of research looks specifically at freight vehicle parking choices for purposes of deliveries to street retail, and choice impacts on travel time/uncertainty, congestion, and emissions. However, little attention was given to large urban freight traffic generators, e.g., shopping malls and commercial buildings with offices and retail. These pose different challenges to manage freight vehicle parking demand, due to the limited parking options. To study these, we propose an agent-based simulation approach which integrates data-driven parking-choice models and a demand/supply simulation model. A case study compares demand management strategies (DMS), influencing parking choices, and their impact in reducing freight vehicle parking externalities, such as traffic congestion. DMS include changes to parking capacity, availability, and pricing as well as services (centralized receiving) and technology-based solutions (directed parking). The case study for a commercial region in Singapore shows DMS can improve travel time, parking costs, emission levels and reducing the queuing. This study contributes with a generalizable method, and to local understanding of technology and policy potential. The latter can be of value for managers of large traffic generators and public authorities as a way to understand to select suitable DMS.

Freight operations in city centers: A land use conflict in urban planning

Municipalities in Colombia have insubstantially developed methods and technical support to diagnose and plan the supply chain in consolidated urban spaces. This situation becomes critical due to strong illegal occupation and deterioration of public space, overcrowding by vulnerable populations, informal commerce and common delinquency. This research paper aims to present a diagnosis of loading and unloading operations in Cali’s city-center and, subsequently, to study some improvement strategies considering stakeholders’ opinion. A combination of methodologies permitted obtaining relevant results that shed light on possible innovative actions to reduce land use conflict in city centers of medium sized cities in emerging countries. The results revealed that the freight parking offer was very scarce and concentrated on bay infrastructure and on-street parking, increasing waiting times and congestion. Parking was not controlled, and illegal practices were present throughout the day, including pedestrian path occupation. A combined set of strategies, such as cargo cycle operation and mini-hubs, could raise the parking offer and adapt to different demand conditions in terms of schedules and vehicles sizes. Cooperation between private stakeholders and the strength of institutional structure is essential to harmonize urban freight evaluations and operations with sustainable mobility policies in city centers. Some strategies require a strong set of public incentives, including tax benefits and logistic land use implementation with price controls. City Centers requires loading and unloading zones and logistic land, besides traffic management policies. Logistic land use is disappearing in central urban zones, which is contradictory and is producing a perverse effect. This is an analysis that reveals the importance of planning mixed land uses that include logistics and different vocations of public space.

Identification of Potential Freight Parking Locations in Urban Areas

Truck parking is a crucial element of the United States’ transportation system as it provides truckers with safe places to rest and stage for deliveries. Demand for truck parking spaces exceeds supply and shortages are especially common in and around urban areas. Freight operations are negatively affected as truck drivers are unable to park in logistically ideal locations. Drivers may resort to unsafe practices such as parking on ramps or in abandoned lots. This report seeks to examine the potential parking availability of vacant urban parcels by establishing a methodology to identify parcels and examining whether the identified parcels are suitable for truck parking. Previous research has demonstrated that affordable, accessible parcels are available to accommodate truck parking. When used in conjunction with other policies, adaptation of urban sites could help reduce the severity of truck parking shortages. Geographic information system parcel and roadway data were obtained for one urban area in each of the 10 Mid America Association of Transportation Officials region states. Area and proximity filters were applied followed by spectral analysis of satellite imagery to identify candidate parcels for truck parking facilities within urban areas. The automated processes created a ranked short list of potential parcels from which those best suited for truck parking could be efficiently identified for inspection by satellite imagery. This process resulted in a manageable number of parcels to be evaluated further by local knowledge metrics such as availability and cost, existing infrastructure and municipal connections, and safety.

A Policy-Sensitive Model of Parking Choice for Commercial Vehicles in Urban Areas

Understanding factors that drive the parking choice of commercial vehicles at delivery stops in cities can enhance logistics operations and the management of freight parking infrastructure, mitigate illegal parking, and ultimately reduce traffic congestion. In this paper, we focus on this decision-making process at large urban freight traffic generators, such as retail malls and transit terminals, that attract a large share of urban commercial vehicle traffic. Existing literature on parking behavior modeling has focused on passenger vehicles. This paper presents a discrete choice model for commercial vehicle parking choice in urban areas. The model parameters were estimated by using detailed, real-world data on commercial vehicle parking choices collected in two commercial urban areas in Singapore. The model analyzes the effect of several variables on the parking behavior of commercial vehicle drivers, including the presence of congestion and queueing, attitudes toward illegal parking, and pricing (parking fees). The model was validated against real data and applied within a discrete-event simulation to test the economic and environmental impacts of several parking measures, including pricing strategies and parking enforcement.

Predicting car park availability for a better delivery bay management

Abstract 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.

Commercial Vehicle Parking in Downtown Seattle: Insights on the Battle for the Curb

Rapid urban growth puts pressure on local governments to rethink how they manage street curb parking. Competition for space among road users and lack of adequate infrastructure force delivery drivers either to search for vacant spaces or to park in unsuitable areas, which negatively impacts road capacity and causes inconvenience to other users of the road. The purpose of this paper is to advance research by providing data-based insight into what is actually happening at the curb. To achieve this objective, the research team developed and implemented a data collection method to quantify the usage of curb space in the densest urban area of Seattle, Center City. This study captures the parking behavior of commercial vehicles everywhere along the block face as well as the parking activities of all vehicles (including passenger vehicles) in commercial vehicle loading zones. Based on the empirical findings, important characteristics of Seattle’s urban freight parking operations are described, including a detailed classification of vehicle types, dwell time distribution, and choice of curb use for parking (e.g., authorized and unauthorized spaces). The relationship between land use and commercial vehicle parking operations at the curb is discussed. Seattle’s parking management initiatives will benefit from the insights into current behavior gained from this research.

Commercial vehicle parking duration in New York City and its implications for planning

Freight vehicle parking has long been an issue in urban areas; however, literature is limited and little attention has been paid to parking duration. Using field-collected freight parking observation data from New York City, this study estimated a parametric survival model to predict parking durations based on several characteristics of a parked freight vehicle. Results indicated that vehicles delivering different types of items parked for differing durations, while vehicles that parked illegally were likely to park for a shorter period of time. The model was then employed to make predictions regarding how parking durations would differ based on vehicle and delivery characteristics. An elasticity analysis was performed on each significant variable, and planning recommendations were made in order to maximize the amount of vehicles parking legally. Recommendations for future research included expanding the dataset to obtain more accurate estimates and applying the duration model to optimize operations at a business or logistics firm.

Data stories from urban loading bays

PurposeFreight vehicle parking facilities at large urban freight traffic generators, such as urban retail malls, are often characterized by a high volume of vehicle arrivals and a poor parking supply infrastructure. Recurrent congestion of freight parking facilities generates environmental (e.g. pollution), economic (e.g. delays in deliveries) and social (e.g. traffic) negative externalities. Solutions aimed at either improving or better managing the existing parking infrastructure rely heavily on data and data-driven models to predict their impact and guide their implementation. In the current work, we provide a quantitative study of the parking supply and freight vehicle drivers’ parking behaviour at urban retail malls.MethodsWe use as case studies two typical urban retail malls located in Singapore, and collect detailed data on freight vehicles delivering or picking up goods at these malls. Insights from this data collection effort are relayed as data stories. We first describe the parking facility at a mall as a queueing system, where freight vehicles are the agents and their decisions are the parking location choice and the parking duration.ResultsUsing the data collected, we analyse (i) the arrival rates of vehicles at the observed malls, (ii) the empirical distribution of parking durations at the loading bays, (iii) the factors that influence the parking duration, (iv) the empirical distribution of waiting times spent by freight vehicle queueing to access the loading bay, and (v) the driver parking location choices and how this choice is influenced by system congestion.ConclusionsThis characterisation of freight driver behaviour and parking facility system performance enables one to understand current challenges, and begin to explore the feasibility of freight parking and loading bay management solutions.

Truck parking in urban areas: Application of choice modelling within traffic microsimulation

Urban truck parking policies include time restrictions, pricing policies, space management and enforcement. This paper develops a method for investigating the potential impact of truck parking policy in urban areas. An econometric parking choice model is developed that accounts for parking type and location. A traffic simulation module is developed that incorporates the parking choice model to select suitable parking facilities/locations. The models are demonstrated to evaluate the impact of dedicating on-street parking in a busy street system in the Toronto CBD. The results of the study show lower mean searching time for freight vehicles when some streets are reserved for freight parking, accompanied by higher search and walking times for passenger vehicles.

Freight-Trip Generation Model

Freight demand models are important analytical tools for estimating demand per retail establishment, which is assumed as the total vehicle arrivals for loading and unloading purposes. Urban freight parking demand prediction is particularly useful in the context of transportation planning to determine, for example, infrastructure options and investment scale (e.g., freight parking). Achieving a balance between the predictive capabilities of the model and the feasibility of application is a challenge, as data regarding the population of retail establishments are frequently minimal. An establishment-based freight survey was used to collect data for 604 retail establishments in the city of Lisbon, Portugal. The relationship of candidate independent variables (e.g., store sales area and supply chain characteristics) versus the total number of weekly deliveries was investigated. Variables were chosen on the basis of suggested choices from the literature but also considering an exploratory perspective. Various sets of variables were modeled under ordinary least squares (OLS) linear regression and generalized linear model frameworks. Several tests were performed to assess model output quality. The analysis of the variables showed that those more commonly used in practical applications were not necessarily the best predictors of demand. The number of employees was consistently a better predictor than the area of the establishment or warehouse. Establishment category was the dominant variable. Supply-chain-related variables slightly improved the predictive capabilities of the models. OLS models showed better predictive capabilities but suffered from heteroskedasticity problems. Overall, the predictive capabilities of any of the models with the chosen methodology were lower than what is considered acceptable for a practical application.

Parking in the City

This paper provides insight into the magnitude of the freight parking problem in large urban areas and the effectiveness of alternative solution strategies. The paper estimates the demand for parking through the use of freight trip generation estimates and estimates the supply of parking on the basis of curb space. The paper discusses strategies for the management of freight parking demand developed by governmental agencies and other organizations. In addition, the paper proposes an approximate methodology to quantify freight parking demand and on-street parking availability. Parking demand is expressed as a function of the freight trip generation of individual establishments, and parking availability is estimated to be a function of curb space dimensions and commercial vehicle characteristics. Empirical findings are provided. New York City is used as a case study. From the analyses and results, the paper provides a set of policy recommendations to help mitigate the identified issues.