This paper provides a systematic review of studies that model future autonomous vehicles (AVs) in urban mobility and logistics using an agent-based model (ABM). Eighty relevant papers published between 2015–2020 are included. This review evaluates the quality of each model through the newly developed AAODD (Autonomous vehicle Agent-based Overview, Design concepts and Details) protocol in four perspectives:

Earth observation (EO) capabilities to produce up-to-date geographical information on slums over large areas supporting urban planning and evidence-based policymaking are largely acknowledged. Most EO studies typically use a data-driven approach without an understanding of end-user requirements. This study addresses this gap by aligning EO methods with societal needs and concerns using a user-driven

Despite the nobody-at-home situation having induced several problems, including home burglaries and home-delivery failure, this situation is not comprehensively understood yet. A recent study has examined and developed temporal profiles of households with the situation of every member out-of-home (HEMO) or nobody-at-home, using household travel surveys. However, the variation of HEMO across different

Ridesourcing service providers have expanded rapidly in recent years, but accessibility for people with disabilities is an ongoing issue. UberWAV was launched in September 2015, providing wheelchair accessible vehicles equipped with a rear-entry ramp, winch, and restraints for riders who travel with a wheeled mobility aid. The objectives of this study were 1) to assess the equity performance of UberWAV

The task of identifying similar regions within and between cities is an important aspect of urban data science as well as applied domains such as real estate, tourism, and urban planning. Regional similarity is typically assessed through comparing socio-demographic variables, resource availability, or urban infrastructure. An essential dimension, often overlooked for this task, is the spatiotemporal

Traffic congestion is an ever-increasing issue across urban environments in the US. One potential mitigation strategy is to improve our understanding of how the geographical patterns of urban land use influence congestion. Unfortunately, there is no consensus regarding if more sprawling or dense urban morphologies help mitigate congestion issues. To potentially clarify the conflicting findings of previous

Recently, people are increasingly interested in understanding broader and longer-term individual activity-travel behaviors across days or even weeks. However, the relationship of individual daily activity-travel behaviors over multi-days only remains partially revealed, especially between workdays and day-offs. Thus, we develop an effective framework to extract individual daily activity-travel patterns

The question “which city is the greenest” sounds trivial, but in reality, this question contains statistical ambiguities. In this study, we approach this issue by ranking cities by green space shares. However, we do not base our ranking only on one green parameter and the commonly used administrative boundaries. Instead, we broaden access to rankings through several approaches: First, we calculate

Using publicly available traffic camera data in New York City, we quantify time-dependent patterns in aggregate pedestrian foot traffic. These patterns exhibit repeatable diurnal behaviors that differ for weekdays and weekends but are broadly consistent across neighborhoods in the borough of Manhattan. Weekday patterns contain a characteristic 3-peak structure with increased foot traffic around 9:00 am

Spatial crime simulations contribute to our understanding of the mechanisms that drive crime and can support decision-makers in developing effective crime reduction strategies. Agent-based models that integrate geographical environments to generate crime patterns have emerged in recent years, although data-driven crime simulations are scarce. This article (1) identifies numerous important drivers of

We propose a scalable agent-based crime simulation model based on the routine activity theory. It uses census data and time geography to create a synthetic population with residences and job locations, commuting schedules, and daily routines constrained by disposable time that are more representative than existing models. The time and location of crime incidents in this model are determined by random

The diversity and density of urban functions have been known to affect urban vibrancy positively, but the relation between the two has not been empirically examined; if high density is associated with low diversity in an area, its vibrancy may not increase. To obtain a better understanding of the metabolism of cities and directions for urban planning interventions, this paper offers empirical evidence

It is important to quantify human heat exposure in order to evaluate and mitigate the negative impacts of heat on human well-being in the context of global warming. This study proposed a human-centric framework to examine human personal heat exposure based on anonymous GPS trajectories data mining and urban microclimate modeling. The mean radiant temperature (Tmrt) that represents human body's energy

Understanding households' behaviour in residential relocation timing is of great importance in the field of transport engineering and economics. This research aims to develop a residential relocation model by considering the potential dynamic impacts of other households' decisions and variables, including economic and demographic attributes, housing features, intra-household decision-making structures

Polycentric urban development is gaining momentum in both scholarly research and real-life practice. This brings new demand for planning support systems to simulate and analyse the urban spatial structure in terms of polycentricity under various urban policy scenarios. With the help of emerging urban data, urban simulation techniques, and network science, this study proposes a workflow to simulate

3D maps are increasingly useful for many applications such as drone navigation, emergency services, and urban planning. However, creating 3D maps and keeping them up-to-date using existing technologies, such as laser scanners, is expensive. This paper proposes and implements a novel approach to generate 2.5D (otherwise known as 3D level-of-detail (LOD) 1) maps for free using Global Navigation Satellite

Social segregation research has a long tradition in urban studies. Usually, these studies focus on the residential dimension, using official registries (e.g., census data), which show population distribution at night. Nevertheless, these studies disregard the fact that the population in cities is highly mobile, and its spatial distribution dramatically changes between night and day. The emergence of

The number of catastrophic events such as extreme rainfalls and hurricanes has been growing. These events pose a major threat to the life safety and economic prosperity of urban regions. Flood control networks play a pivotal role in mitigating the risk associated with the stormwater generated by extreme rainfalls and hurricanes. The objective of this study is to propose a framework to examine the vulnerability

Urban Densification Suitability Analysis (UDSA) approach has been proposed using the GIS-based Logic Scoring of Preference (LSP) decision method. Our goal is to provide the methodology for the evaluation of suitability of locations for high density urban development to facilitate the spatial decision-making process and help justifiable urban planning. The main objectives of this research study are

This paper presents a novel model framework for complex urban traffic systems based on the interconnection of a dynamical multi-agent system in a macroscopic level. The agents describe all the types of street segments, intersections, sources and sinks of cars, modelling the behavior of the flow of vehicles through them as simple differential equations. These agents include the phenomena of changes

When getting on and off the taxis, people prefer to choose around landmarks, such as a shopping mall, the gate of a residential unit, or a road intersection. This preference leads to multiple local peaks in spatial density, existing in both highly popular and less popular regions. These multiple local peaks provide a natural form to describe small-scale hotspots where the pick-up and drop-off events

The objective of the p-median problem is to identify p source locations and map them to n destinations while minimizing the average distance between destinations and corresponding sources. Several heuristic algorithms have been developed to solve this general class of facility location problems. In this study, we add to the current literature in two ways: (1) we present a thorough evaluation of existing

The continued increase in average and extreme temperatures around the globe is expected to strike urban communities more harshly because of the urban heat island (UHI). Devising natural and design-based solutions to stem the rising heat has become an important urban planning issue. Recent studies have examined the impacts of 2D/3D urban land-use structures on land surface temperature (LST), but with

Urban scaling laws assume that the performance of a city largely relies on its urban population size. However, two cities with the same population size may have vastly different economic outputs, which reveals that factors apart from urban size (a measurement of intra-urban interactions) determine their economic outputs. Economic production is essentially the product of social interactions. Urban population

Real estate property listings use specific language to market properties to a target buyer – typically one that will garner the largest profit. As home-seekers have different preferences for house characteristics and neighborhood amenities, the words used to advertise homes are expected to vary according to the type of neighborhood and expected homebuyer. In this article, we develop a framework for

Delineating urban functional use plays a key role in understanding urban dynamics, evaluating planning strategies and supporting policymaking. In recent years, Points of Interest (POI) data, with precise geolocation and detailed attributes, have become the primary data source for exploring urban functional use from a bottom-up perspective, using local, highly disaggregated, big datasets. Previous studies

Transformations of agriculture and natural lands to urban use are dominant and well known effects of urbanization, but consistent and reliable methods to map regional-scale land transitions are limited. Herein, we develop a land use classification approach to assess spatiotemporal patterns of urban expansion and associated land use changes. We utilized a 33-year annual time series (1984–2016) of fractional

Collection and transportation of solid waste are costly for municipal budgets. This study challenges the use of existing administrative boundaries in waste management applications. By reducing the spread (standard deviation) of parameters (landfills, populated places, and roads), efficient and practical waste management regions are created. A novel alteration to the Centroidal Voronoi Tessellation

Portable sensing, in which lightweight mobile sensors are used to measure stimuli, events, and human behavior, is a new and disruptive data collection paradigm. It has several methodological advantages compared to traditional methods and is suitable for investigating the dynamism of increasingly mobile and urban societies. In this article, we discuss the motivations behind the use of portable sensing

Urban energy consumption is expected to continuously increase alongside rapid urbanization. The building sector represents a key area for curbing the consumption trend and reducing energy-related emissions by adopting energy efficiency strategies. Building age acts as a proxy for building insulation properties and is an important parameter for energy models that facilitate decision making. The present

The optimisation of surveillance and detection systems comprised of specialised cameras is a well-known problem in the operations research literature. In these problems, the aim is to locate optimal camera sites so that their combined coverage with respect to some area of interest – called a cover zone – is maximised. The standard approach is to maximise cover with respect to a single cover zone, and

Sustainability policies to mitigate transportation energy impacts on the urban environment are urgently needed. Energy prediction models provide critical information to decision-makers who develop sustainability policies to reduce energy use and emissions. We present a transportation energy model (TEM) that uses Explainable Artificial Intelligence (XAI) methods to predict household transportation energy

Nowadays, biking is flourishing in many Western cities. While many roads are used for both cars and bicycles, buffered bike lanes are marked for the safety of cyclists. In many cities, segregated paths are built up to have physical separation from motor vehicles. These types of biking ways are regarded as attributes in geographic information system (GIS) data. This information is required and important

Sustainable urban design or planning is not a LEGO-like assembly of prefabricated elements, but an embryo-like growth with persistent differentiation and adaptation towards a coherent whole. The coherent whole has a striking character – called living structure – that consists of far more small substructures than large ones. To detect the living structure, natural streets or axial lines have been previously

Pedestrians, while walking alone, feel unsafe and vulnerable in certain outdoor spaces at certain times of the day. This fear of victimisation often leads pedestrians to avail costlier alternatives, such as taking viable detours or abandoning walking altogether and switching to alternative forms of transport. This fear reduces the appeal of walking, the most basic transport mode, and one that is essential

This work examines different spatial and sociodemographic models for predicting residential fire counts at the census tract level for 118 U.S. fire departments across 25 states. The models give five-year forecasts of residential fire counts for 3392 census tracts which contain over 13 million residents in total. All models described in this paper train on fire incident data from the National Fire Incident

Spatio-temporal patterns of human activities can be affected by events, such as extreme weather. Events cause anomalies that could be expressed by abnormal activity patterns deviating from the inherent ones. The detection of spatio-temporal anomalies thus helps to understand the implicit influencing mechanism with which the external factors affect human activities. Existing methods of spatio-temporal

Multi-Criteria Decision Analysis (MCDA) offers a unique analytical lens for examining socio-economic indicators within cities. Competing, interrelated criteria are combined to produce a single weighted score for each location or spatial unit. The Toronto Foundation's Vital Signs report provides an annual snapshot of Toronto's quality of life, using ten indicator categories. While the report is published

Flood risk maps are essential sources of information for flood risk management (FRM) decisions. Commercial flood models used by the insurance industry are rarely studied in the academic literature which has led to difficulties in understanding their sources of uncertainty and opportunities for improvement. This paper compares regions and residential properties identified as exposed to floods by an

Social sensing is an analytical method to study the interaction between human and space through extracting reliable information from massive volunteered information data. During the ongoing COVID-19 pandemic, there are a large number of Internet social sensing data. However, most of them lack geographic attribute. In order to resolve this problem, this paper proposes a convolutional neural network

This paper uses movement as a marker to study interactions in humans and animals to better understand their collective behaviors. Interaction is an important driving force in social and ecological systems. It can also play a significant role in the transmission of infectious diseases and viruses as witnessed during the ongoing COVID-19 pandemic. Although a number of approaches have been developed to

The drug-related problem poses a serious threat to human health and safety. Previous studies have associated drug places with factors related to place management and accessibility, often at several scattered places, as data at the micro level are hard to obtain at a city-wide scale. Google Street View imagery presents a new source for deriving micro built environment characteristics, including place

Many parts of the world experience severe episodes of flooding every year. In addition to the high cost of mitigation and damage to property, floods make roads impassable and hamper community evacuation, movement of goods and services, and rescue missions. Knowing the depth of floodwater is critical to the success of response and recovery operations that follow. However, flood mapping especially in

While Google Street View (GSV) has been increasingly available for large-scale examinations of urban landscapes, little is known about how to use this promising data source more cautiously and effectively. Using data for Santa Ana, California, as an example, this study provides an empirical assessment of the sensitivity of GSV-based streetscape measures and their variation patterns. The results show

Detecting borders of urban activity spaces is essential for understanding urban dynamic structures. The emerging big geo-data help to extract valuable knowledge about the relationship between urban structures and human activities at fine granularities. Despite the well-developed urban structure and transportation network design technology, barriers attenuating intra-urban travel still exist as borders

Mobile phone data help us to understand human activities. Researchers have investigated the characteristics and relationships of human activities and regional function using information from physical and virtual spaces. However, how to establish location mapping between spaces to explore the relationships between mobile phone call activity and regional function remains unclear. In this paper, we employ

Carsharing services aim at offering short-term car rentals. Those rentals can be offered in different modalities, such as round-trip and one-way. This work simulates the dynamics of possible carsharing clients searching for resources availability and using the carsharing service. Clients can rent a vehicle if there is an available car at the client's origin station and an available parking slot at

Twitter users mention cities in the context of tourist attractions or events, such as protests or games, thus forming a network between cities from which they tweet and cities that they tweet about. This study tackles the challenge of explaining why users tweet about cities outside of their own by analyzing an underlying network of city mentions on Twitter. It applies graph theory as well as various

Flooding has been one of the major issues that have seriously hampered the developments of the urban systems, as well as threatened the urban water environments. To mitigate the impacts of floods, it is highly important to identify effective evacuation plans with the aid of computer-based methods. However, effectively identifying the dynamic evacuation route based on the evolution of flooding status

Countries in Southeast Asia have been developing quickly from a predominantly rural to predominantly urban society, leading to a rapid increase in urban land. This increase in urban land has mainly occurred in river deltas and floodplains, exposing humans and human assets to flood hazard. Here we present an assessment of current and future flood risk in five countries of mainland Southeast Asia, using

Efficiently predicting traffic congestion benefits various traffic stakeholders, from regular commuters and logistic operators to urban planners and responsible authorities. This study aims to give a high-quality estimation of traffic conditions from a large historical Floating Car Data (FCD) with two main goals: (i) estimation of congestion zones on a large road network, and (ii) estimation of travel

How to reveal the hierarchical structures of urban space has long posed a challenge for researchers. Yet, revealing such structures is of fundamental importance to our understanding of multiscale urban organization. This study, therefore, built a spatial interaction network based on a massive body of human movement data to derive the hierarchical organization of ranked urban clusters. These urban clusters

Abstract Based on an offender spatial decision-making perspective, this burglary target location choice study aims to understand how physical and social barriers affect why residential burglars commit their crimes at particular locations in a major Chinese city. Using data on 3860 residential burglaries committed by 3772 burglars between January 2012 and June 2016 in ZG city, China, conditional logit

Abstract Utility networks comprise a fundamental part of our complex urban systems and the integration of digital representations of these networks across multiple spatial scales can be used to help address priority challenges. Deteriorating water utility infrastructure and low routing redundancy result in network fragility and thus supply outages when assets fail. Water distribution network configurations

Abstract As the distinction between online and physical spaces rapidly degrades, social media have now become an integral component of how many people's everyday experiences are mediated. As such, increasing interest has emerged in exploring how the content shared through those online platforms comes to contribute to the collaborative creation of places in physical space at the urban scale. Exploring

Abstract Cities are continually evolving through adjustments to zonings. The potential to integrate more equitable and effective value capture mechanisms into the rezoning process makes estimation of increases in land value from rezoning of interest to urban planners. The use of Residual Land Valuation (RLV), while accepted practice in development feasibility calculations, tends to be absent from the

Abstract Since new (Power) Transmission Lines (TLs) can have a long-term effect on the makeup of a landscape and on the human living space, it should be expected that the route of any new TL will be based on objective criteria that take into account the views of the public. Geographic Information Science (GIScience) provides powerful tools that assist in the determination of feasible locations for

Geospatial data conflation is the process of combining two datasets to create a better one. It has received increased research attention due to the emergence of new data sources and the need to combine information from these sources in spatial analyses. Many conflation methods exist to date, ranging from simple ones based on spatial join, to sophisticated methods based on statistics and optimization

Extracting hidden information from human mobility patterns is one of the long-standing challenges of urban studies. In addition, exploring the relationship between urban functional structure and traffic spatial interaction pattern has long been of interest. Recently, vehicle GPS trajectory data emerged as a popular data source for revealing human mobility patterns and urban functions. However, few