Pedestrian Model Research Articles

Investigating pedestrian stepping characteristics via intrinsic trajectory

Investigating pedestrian stepping is essential for pedestrian dynamics research, aiding in understanding pedestrian behavior and crowd modeling. However, how to calculate the basic step metrics is still controversial, and the differences between straight walking and turning steps are often overlooked in past studies. In this work, we proposed the trajectory-based measurement to more accurately calculate the step metrics and further analyze the differences between the straight walking and turning steps. The trajectory-based measurement takes the intrinsic trajectory of the pedestrian as the reference frame to guide a more universal measurement for stepping characteristics. By applying the proposed trajectory-based measurement to revisit the dataset of a single-file experiment, we identify differences between the straight walking step and the turning step from multiple perspectives. The results show that when density is low, straight walking steps exhibit larger step velocity and length, whereas turning steps display more unbalanced lateral motion. As density increases, both types of steps demonstrate greater forward motion imbalance, while pedestrians prefer to take the step on the outer side of the turn to propel their forward motion when taking turning steps. These findings deepen our understanding of pedestrian stepping behavior and provide valuable insights for future studies of pedestrian dynamics.

Road Design and Traffic Detection Methods for Autonomous Driving Scenarios

With the rapid promotion of autonomous driving technology, it is extremely important to scientifically anticipate the related technologies and analyze their possible impact on urban road systems. The accuracy of detection and localization of traffic elements of autonomous driving is closely related to the ability of autonomous driving devices to make control decisions and the safety of autonomous driving. The study designs a new high-speed road driving scheme based on autonomous driving by analyzing the challenges related to urban traffic that may be brought about by unmanned driving. On the basis of the faster R-CNN algorithm, the context information around the target is introduced to locate and detect small-scale traffic signs. A new pedestrian detection model is designed, which is based on the feature pyramid network and introduces the SE module to highlight the features of the visible part of the pedestrian and reduce the missed detection rate caused by inter-class occlusion. The improved traffic sign detection framework improves the detection accuracy by 18.91% compared to the original faster R-CNN, while the enhanced pedestrian inspection method improves the detection accuracy by 14.00%. For both traffic sign detection and pedestrian detection accuracy and speed are improved compared to the original method.

How do age compositions affect pedestrian dynamics on stairways? Findings from controlled experiments

Understanding pedestrian dynamics for different age compositions is paramount for enhancing their safety level on stairways in normal and evacuation conditions. In this study, we illustrate the findings of a series of single-file pedestrian flow experiments on stairways. This experiment was carried out with 131 participants having different age compositions, including preschool students (4–5 years), primary school students (8–10 years), middle school students (12–14 years), middle-aged pedestrians (27–57 years), and the elderly pedestrians (63–81 years). We collect data using three experimental settings: individual experiments with age homogeneity, single-file pedestrian experiments with age homogeneity, and single-file pedestrian experiments with age-mixed. Our findings show that age plays a substantive non-linear impact on individual unconstrained ascending and descending speeds. In addition, we developed a linear regression model that predicts the pedestrians’ free speed of different ages on stairways. Then, the effects of age compositions on pedestrian dynamics with fundamental diagrams (i.e., speed-density and specific flow-density relations) are analysed, and the maximum specific flow of age homogeneity is higher than that of age-mixed scenarios. Finally, we illustrate how the headway distance impacts participants’ ascending and descending speeds in both age-homogeneity and age-mixed conditions. As such, these new experimental results can provide the foundation for future calibration and validation of pedestrian evacuation models as well as insight into safety guidelines.

Exploring crossing times and congestion patterns at scramble intersections in pedestrian dynamics models: A statistical analysis

In this study, we present a model designed to unravel the statistical complexities of pedestrian crossing times and related physical quantities in a unique pedestrian intersection, specifically a double intersecting diagonal crossing. Our model employs an agent-based stochastic process based on a lattice gas dynamics to describe a four-species interaction on an underlying square lattice representing the intersection. This model incorporates four static floor fields guiding each species within the scenario. Our findings emphasize the critical role that the density of highly driven pedestrians plays in the statistics of crossing times by triggering the transition from a normal distribution for low densities to a uniform distribution for a crowded scenario. In the steady state, we mapped the system mobility and showed that an anomalous mobile phase emerges when considering a scenario with high density of poorly driven pedestrians. To monitor these phenomena, we focus on two variables: directed mobility and average distance between agents. We conclude our analysis by overcoming a crucial limitation of the lattice gas model with the introduction of viscosity effects among pedestrians. We observe that mixed state regime emerges as the system presents a mobile-to-immobile phase transition when viscosity effects faints. Our research sheds light on the nuanced dynamics of scramble intersections, contributing to a deeper understanding of urban mobility challenges.

Dependent Hidden Markov Model for pedestrian intention prediction: considering Multivariate Interaction Force

Accurately recognizing and predicting pedestrian intentions is crucial for autonomous vehicle safety. However, existing prediction models often fail to comprehensively consider interactions between various traffic elements, resulting in suboptimal accuracy and robustness, especially in complex environments. To address this, we propose a pedestrian intention prediction model combining the Multivariate Interaction Force (MIF) model and a Dependent Hidden Markov Model (DE-HMM) for unsignalized midblock crossings. The MIF model captures dynamic interactions among pedestrians, vehicles, and the environment, while DE-HMM uses MIF data and pedestrian head orientation for predictions. Our model achieves 91.5% accuracy in recognizing crossing intentions, and 88.7% and 85.1% accuracy for predictions 0.5s and 1s ahead, respectively, outperforming current mainstream models and demonstrating strong robustness in special scenarios.

YOLOv7-PD: Incorporating DE-ELAN and NWD-CIoU for Advanced Pedestrian Detection Method

In the pedestrian detection task, the excessive depth of the convolutional network in YOLOv7 results in an abundance of background feature information, thereby posing challenges for the model to accurately locate and detect pedestrians, particularly in small-scale or heavily occluded scenarios. To handle this problem, we propose a pedestrian detection model called YOLOv7-PD, to strengthen the accuracy of detecting small-scale pedestrians and occluded pedestrians. First of all, we propose an improved module called DE-ELAN, an improvement on the existing E-ELAN module, which is based on Omni-Dimensional Dynamic Convolution (ODConv). This module leverages four complementary attention types to enhance feature extraction, capturing rich contextual information. Then, we propose a lightweight receptive field enhancement module called light-REFM, which constructs a pyramid structure and acquires fine-grained multi-scale information through dilated convolutions of different sizes. Finally, we propose an improved regression loss function based on the Normalized Wasserstein Distance (NWD) that combines NWD with Complete-IoU (CIoU), enabling precise position and feature capture for small targets. On the Citypersons dataset, YOLOv7-PD outperforms YOLOv7, improving the average precision (AP) by 7% and reducing the miss rate by 2.58%. Experiments on three challenging pedestrian detection datasets demonstrate a balance between precision and speed, achieving excellent performance.

Evaluating the Efficacy of Agent-Based Modeling in Analyzing Pedestrian Dynamics within the Built Environment: A Comprehensive Systematic Literature Review

The dynamics of pedestrian behavior within the built environment represent a multifaceted and evolving field of study, profoundly influenced by shifts in industrial and commercial paradigms. This systematic literature review (SLR) is motivated by the imperative to comprehensively investigate and assess the built environment through the lens of pedestrian modeling, employing advanced modeling tools. While previous scholarship has explored the interplay between the built environment and pedestrian dynamics (PD), there remains a conspicuous gap in research addressing the utilization of agent-based modeling (ABM) tools for a nuanced evaluation of PD within these contexts. The SLR highlights the essential and practical benefits of using ABM to study PD in built environments and combine related theories and practical projects. Beyond theoretical discussions, it emphasizes the real-world contributions of ABM in understanding and visualizing how people behave in urban spaces. It aims to provide deep insights for both researchers and urban planners. By thoroughly examining recent research, it not only explores the practical uses of ABM but also reveals its broad implications for various aspects of pedestrian behavior in built environments. As a result, this SLR becomes a key resource for understanding the crucial role of ABM in studying the complexities of our surroundings. The findings and discussion here highlight ABM’s vital role in bridging the gap between theory and practice, improving our understanding of pedestrian behavior in urban settings. Furthermore, this study outlines promising avenues for future research, thereby fostering continued exploration and innovation in the dynamic realm of pedestrian behavior within built environments.

DELTA: Integrating Multimodal Sensing with Micromobility for Enhanced Sidewalk and Pedestrian Route Understanding.

Urban environments are undergoing significant transformations, with pedestrian areas emerging as complex hubs of diverse mobility modes. This shift demands a more nuanced approach to urban planning and navigation technologies, highlighting the limitations of traditional, road-centric datasets in capturing the detailed dynamics of pedestrian spaces. In response, we introduce the DELTA dataset, designed to improve the analysis and mapping of pedestrian zones, thereby filling the critical need for sidewalk-centric multimodal datasets. The DELTA dataset was collected in a single urban setting using a custom-designed modular multi-sensing e-scooter platform encompassing high-resolution and synchronized audio, visual, LiDAR, and GNSS/IMU data. This assembly provides a detailed, contextually varied view of urban pedestrian environments. We developed three distinct pedestrian route segmentation models for various sensors-the 4K camera, stereocamera, and LiDAR-each optimized to capitalize on the unique strengths and characteristics of the respective sensor. These models have demonstrated strong performance, with Mean Intersection over Union (IoU) values of 0.84 for the reflectivity channel, 0.96 for the 4K camera, and 0.92 for the stereocamera, underscoring their effectiveness in ensuring precise pedestrian route identification across different resolutions and sensor types. Further, we explored audio event-based classification to connect unique soundscapes with specific geolocations, enriching the spatial understanding of urban environments by associating distinctive auditory signatures with their precise geographical origins. We also discuss potential use cases for the DELTA dataset and the limitations and future possibilities of our research, aiming to expand our understanding of pedestrian environments.

A New Method for Traffic Participant Recognition Using Doppler Radar Signature and Convolutional Neural Networks.

The latest survey results show an increase in accidents on the roads involving pedestrians and cyclists. The reasons for such situations are many, the fault actually lies on both sides. Equipping vehicles, especially autonomous vehicles, with frequency-modulated continuous-wave (FMCW) radar and dedicated algorithms for analyzing signals in the time-frequency domain as well as algorithms for recognizing objects in radar imaging through deep neural networks can positively affect safety. This paper presents a method for recognizing and distinguishing a group of objects based on radar signatures of objects and a special convolutional neural network structure. The proposed approach is based on a database of radar signatures generated on pedestrian, cyclist, and car models in a Matlab environment. The obtained results of simulations and positive tests provide a basis for the application of the system in many sectors and areas of the economy. Innovative aspects of the work include the method of discriminating between multiple objects on a single radar signature, the dedicated architecture of the convolutional neural network, and the use of a method of generating a custom input database.

Modeling pedestrian activity in cities with urban network analysis

The global climate-change crisis, along with public health and economic competitiveness challenges in cities around the world have underscored the need for analytic tools to examine the relationship between city design and sustainable mobility. Car-centered travel demand models and land-use-transportation interaction models have historically analyzed zone-to-zone trips along major roadways, largely omitting pedestrian and bicycle trips and creating a gap in the ability for planners and urban designers to systematically assess non-motorized outcomes of development interventions. We present the Urban Network Analysis tools to address this gap. UNA tools offer an accessibility-based framework for analyzing how built environments influence pedestrian travel in both existing and newly planned built environments. Developed as a free plugin for Rhinoceros 3D since 2015 and applied in several cities and research projects internationally, this paper describes the current Urban Network Analysis modeling framework and discusses the unique contributions the framework offers compared to existing pedestrian modeling approaches. Using Somerville, MA as case example, we demonstrate several commonly used functions for planners: examining pedestrian accessibility over networks; identifying critical walking routes to destinations; estimating foot-traffic on street segments; identifying frustration points for pedestrians; and evaluating how development changes may impact pedestrian activity in their vicinity. Such analyses can provide analytic evidence to pedestrian infrastructure planning and investment, and enable planners, designers, and policy makers to prioritize projects that increase sustainable mobility outcomes.

Design and Validation of a Fiber-Reinforced Polymer Cable-Stayed Pedestrian Bridge: Human-Induced Actions vs. Comfort Levels.

The investigation into advanced structural materials, such as composite materials, has revealed numerous possibilities within the field of bridge engineering. Glass-fiber-reinforced polymers (GFRPs) are notable among these materials, particularly in footbridge construction, encompassing both arch and cable-stayed designs. While GFRPs boast advantages, such as their high strength-to-weight ratio, they may exhibit some deficiencies, particularly when subjected to dynamic loads induced by wind or pedestrian forces. Two noteworthy global examples are the Lleida arch bridge (Spain, 2001) and the Aberfeldy cable-stayed bridge (Scotland, 1992). These structures have recently undergone comprehensive studies by the authors to assess their behavior when subjected to specific conditions regarding pedestrian traffic and vibrations induced by under-passing trains, as far as Lleida is concerned. The methodologies employed in these studies are detailed herein, incorporating the relevant scientific literature and technical regulations that provide guidance on fundamental principles for bridge design, pedestrian modelling, and acceleration thresholds aimed at minimizing discomfort. While the framework of principles is clear, the regulations are extensive, requiring designers to have a comprehensive understanding of the diverse outcomes achievable through various approaches. Therefore, the provided state-of-the-art overview serves as a roadmap for assessing the performance of an innovative cable-stayed bridge recently proposed by one of the authors. Initially designed with six spans, this prototype has been reconfigured here as a three-span train station overpass. The analyses conducted allowed for the assessment of induced accelerations. According to current accredited standards, the resulting comfort classification is considered minimal, even if, for crowded conditions, more specific studies are required.

Correlation Study of Commercial Street Morphology and Pedestrian Activity in Cold Region Summers under Thermal Comfort Guidance: A Case Study of Sanlitun, Beijing

Pedestrian vitality in commercial streets is influenced by various factors, among which the spatial form of the street and the resulting thermal environment have a significant impact. This study, from the perspective of thermal comfort, combines thermal comfort simulation with pedestrian simulation to establish an optimization model based on pedestrian vitality. The model aims to analyze and quantify the impact of street spatial form on thermal comfort and pedestrian vitality, providing a comprehensive evaluation of optimization schemes for commercial street spaces. Firstly, the study identifies the levels of spatial design parameters for commercial streets and generates optimized design scenarios for commercial street spaces. Using the simulation platforms Rhino 7 Grasshopper and MATLAB R2023a, a pedestrian simulation model guided by thermal comfort is constructed and validated against empirical data. Next, the influence of commercial street spatial design parameters on store visitations is assessed, identifying the most critical design parameters. Finally, design strategies for commercial streets are proposed based on vitality-oriented layouts. The results indicate that the spatial form of the street significantly affects store visitations, with the street width-to-height ratio being the most influential factor, followed by street orientation and interface form. NW-SE-oriented streets show a 47.2% higher Total Store Visitations (TSV) value compared to E-W-oriented streets, while E-W streets exhibit a Differential Store Visitation (DSV) value 4.47 times that of NW-SE streets. Streets with a W/H ratio of 0.25 have a 54.9% higher Total Store Visitations value than those with a W/H ratio of 0.9, and streets with a W/H ratio of 0.65 exhibit a Differential Store Visitations value 1.21 times that of streets with a W/H ratio of 0.25. Considering overall street vitality, the study recommends NW-SE- and NE-SW-oriented streets, with a width-to-height ratio between 0.25 and 0.4. The study also proposes strategies for the modification and expansion of streets in different orientations, providing the scientific basis and optimization recommendations for the planning and renovation of commercial streets in cold regions during summer.

Day-to-day behaviour for pedestrians in a circle antipode scenario: experiment and simulation

This study aims to investigate the behavioural evolution patterns of pedestrians who repeatedly traverse the same scenarios. To accomplish this objective, we implemented a repeated circle antipode experiment, ensuring consistent conditions for all participants. This experimental design allows for an effective examination of participant performance across multiple rounds. Our findings revealed that specific walking characteristics experienced significant changes with the progression of successive experiments, and participants demonstrated notable differences in their chosen routes. Recognizing the ability of the day-to-day dynamic model to describe the evolution of network flows and the similarities between traffic and pedestrian flows, we apply the modelling approach of the day-to-day dynamic model to the construction of pedestrian route choice modelling. Consequently, we developed a series of round-to-round pedestrian route choice models to characterize our experiment. These models factored in both historical walking experiences and the influence of neighbours. Our model proved to be reliable, achieving a route choice accuracy of approximately 80% in simulations of circle antipode experiments. The results of this study can provide valuable insights into pedestrian dynamics, aiding in understanding pedestrian behaviour during repetitive walking and facilitating the development of more accurate round-to-round route choice models.

Infrared-Visible Synthetic Data from Game Engine for Image Fusion Improvement

Limited by the shooting scenes and angles of fixed cameras, the existing datasets generally lack many detailed pedestrian models in diverse scenarios. Existing deep learning-based image fusion methods, for this reason, bring about overfitting or insufficient information of fusion results in varying degrees. To address this challenge, A new infrared-visible pedestrian synthetic dataset (GIVF) with a synthetic data tagger (GSDT) is constructed and an improved end-to-end image fusion network (FSGAN) is proposed to validate infrared and visible fusion. In the model, the method uses an auxiliary network to extract features that complement the cascade network of the main path, effectively improving the ability to extract pedestrian texture details. Experimental results show that FSGAN can be well applied to GIVF. By conducting extensive comparative experiments with eight state-of-the-art image fusion methods. FSGAN shows better performance than those comparison methods, especially in the two evaluation indexes visual information fidelity (VIF) and structural similarity measurement (SSIM). Besides, by comparing the quantitative analysis results of various methods, and evaluating the fusion results of real images in complex environments on other three datasets, we conclude that FSGAN can be better applied to GIVF datasets than other popular methods, and has outstanding performance in generalization.

A novel approach for reliable pedestrian trajectory collection with behavior-based trajectory reconstruction for urban surveillance systems

Collecting reliable pedestrian trajectories in pedestrian behavior analysis, trajectories broken by frame sampling and trajectories crossing in multi-object conditions often hinder their performance of existing pedestrian tracking models. Despite attempts to address these issues by performing detection and tracking simultaneously using deep learning algorithms, previous methods still struggle with errors such as mistaking a single pedestrian for multiple pedestrians. We propose a novel approach to efficiently collect and correct pedestrian trajectories with minimized practical errors in multi-object conditions for urban surveillance systems. Our system utilizes a single vision sensor to automatically collects trajectories of multiple pedestrians and employ simple, low-computational algorithms, particularly the Deep simple online real-time tracking (Deep SORT) method, to calibrate the trajectories from tracking-by-detection models. Additionally, our system identifies and merges broken pedestrian trajectories, treating them as potential single trajectories, while considering their spatiotemporal ranges. We evaluate the proposed system by implementing it on real testbed video footage. Our method significantly improves practical errors and achieves more accurate pedestrian trajectories compared to existing models, and exhibits robust characteristics, effectively handling complex situations such as occlusions and crowds.

Evacuation simulation considering pedestrian risk perception under toxic gas diffusion

Toxic gas leakage in densely populated public areas can easily endanger people's safety and social stability. This paper establishes a pedestrian evacuation model based on cellular automata that considers the pedestrians' perception of casualties and environmental risks under the real-time spread of toxic gas. The model simulates the emergency evacuation process of pedestrians and their exit selection behavior in the toxic gas leakage incident at a passenger station. The simulation results indicate that evacuees' dynamic exit selection behavior regarding the risk perception of toxic gas incidents positively affects pedestrian evacuation. An optimal perception radius and toxic gas alarm conditions exist that minimize pedestrian casualties while maximizing evacuation efficiency. An increase in the exit change probability keeps pedestrians away from risk but may lead to congestion and reduce evacuation efficiency when the pedestrian density is high. The location of the toxic gas source has different impacts on casualties, and the dynamic exit selection rules prove more effective when the gas source is closer to one of two exits rather than being located between them.

Pedestrian Abnormal Behavior Detection System Using Edge–Server Architecture for Large–Scale CCTVEnvironments

As the deployment of CCTV cameras for safety continues to increase, the monitoring workload has significantly exceeded the capacity of the current workforce. To overcome this problem, intelligent CCTV technologies and server-efficient deep learning analysis models are being developed. However, real-world applications exhibit performance degradation due to environmental changes and limited server processing capacity for multiple CCTVs. This study proposes a real-time pedestrian anomaly detection system with an edge–server structure that ensures efficiency and scalability. In the proposed system, the pedestrian abnormal behavior detection model analyzed by the edge uses a rule-based mechanism that can detect anomalies frequently, albeit less accurately, with high recall. The server uses a deep learning-based model with high precision because it analyzes only the sections detected by the edge. The proposed system was applied to an experimental environment using 20 video streams, 18 edge devices, and 3 servers equipped with 2 GPUs as a substitute for real CCTV. Pedestrian abnormal behavior was included in each video stream to conduct experiments in real-time processing and compare the abnormal behavior detection performance between the case with the edge and server alone and that with the edge and server in combination. Through these experiments, we verified that 20 video streams can be processed with 18 edges and 3 GPU servers, which confirms the scalability of the proposed system according to the number of events per hour and the event duration. We also demonstrate that the pedestrian anomaly detection model with the edge and server is more efficient and scalable than the models with these components alone. The linkage of the edge and server can reduce the false detection rate and provide a more accurate analysis. This research contributes to the development of control systems in urban safety and public security by proposing an efficient and scalable analysis system for large-scale CCTV environments.

Applicability of the Madymo Pedestrian Model for forensic fall analysis

Forensic reconstruction and scenario evaluation are crucial in investigations of suspicious deaths related to falls from a height. In such cases, distinguishing between accidental falls, being pushed or jumping is an important but difficult task, since objective methods to do so are currently lacking. This paper explores the possibility of repurposing a passive rigid body model of a human from commercially available crash simulation software for forensic reconstruction and scenario evaluation of humans dropping from heights. To use this approach, a prerequisite is that the human body model can produce realistic movements compared to those of a real human, given similar environmental conditions. Therefore, this study assessed the validity of the commercially available Simcenter Madymo Pedestrian Model (MPM) for simulating human fall movements. Experimental kinematic and kinetic data was collected from nine participants, who dropped from a height in three different ways: passively tilting over, getting pushed, and jumping. Next, the performance of the MPM in reproducing the kinematics of the experimental falls was assessed by comparing the orientation of the body 0.3 s after platform release. The results show that the MPM currently does not consistently reproduce the experimentally recorded falling movements across multiple falling conditions and outcome measures. The MPM must therefore be adapted if to be used for forensic reconstruction and scenario evaluation, for example by implementing active movement.

Study on pedestrian accessibility from urban transportation hubs to tourist destinations: A case of Shanghai Yuyuan station to Yuyuan garden

Public transportation is a crucial means for tourists to visit urban attractions. This study aims to explore pedestrian accessibility from tourist destinations to urban transportation hubs, using the renowned Yuyuan Garden in Shanghai as a case study and employing spatial syntax methodology. By investigating spatial relationships and accessibility levels from the perspectives of depth and choice. The research finds that the main transportation hubs around the Yuyuan Garden area have a limited capacity to attract visitors, and visitors have limited choices when it comes to using public transportation. Additionally, there are barriers from transportation hubs to the entrance of the tourist area, and some segments of accessibility overlap, posing challenges for the operation of the tourist area, especially during specific festivals. The study emphasizes the significance of the straightforward data acquisition method of spatial syntax for further research on transportation network accessibility, as well as the layout of commercial areas, tourist attractions, and historic districts. However, it also highlights the limitations of spatial syntax, including its abstraction, disregard for temporal attributes, and insufficient qualitative analysis. It acknowledges the subjectivity inherent in pedestrian accessibility and model creation.

The Development of Modeling Shared Spaces to Support Sustainable Transport Systems: Introduction to the Integrated Pedestrian–Vehicle Model (IPVM)

The significance of developing shared road infrastructure in cities throughout the world is growing. Driven by the need to improve traffic management in ways that enhance multiple sustainability outcomes, developing the tools needed to test shared space proposals is becoming more sought after by responsible agencies. This paper reviews approaches to simulation modeling focused on representing and assessing shared spaces, culminating in a new approach presented here called the Integrated Pedestrian–Vehicle Model (IPVM)—a novel framework that combines social force models, car-following models and other algorithms from the robotics domain to better describe both mobility and activity within a shared space. The IPVM recognizes that while shared spaces are inherently multimodal, past efforts have tended to use pedestrian models as a starting point. Most consider the interaction of pedestrians with other pedestrians and static road infrastructure. Shared space models are generally microscopic models that integrate a social force model with a variety of car-following models to describe the interaction between vehicles and pedestrians. However, there is little research and few practical methodologies that address the long-range conflict avoidance between vehicles and pedestrians. This aspect is crucial for accurately representing the desire lines and pathways of pedestrians and active transport users in complex environments like shared spaces. The IPVM describes and visualizes shared road infrastructure with an absence of separating infrastructure between users and outputs. It generates metrics that can be used in conjunction with the latest evaluation approaches to gauge the sustainability credentials of shared space road proposals. Enhanced modeling of shared space solutions can lead to more effective implementation, which can potentially reduce the presence of cars, increase public and active transport use and lead to a more sustainable transport system.