Sustainable Transport System Research Articles

Assessing the environmental benefits of passenger cars electrification in metropolises: A case study of Singapore

Land-based transportation is a significant contributor to global greenhouse gas (GHG) emissions, with internal combustion engine vehicles being the predominant mode of transport in this sector. As the world population grows and urbanization accelerates, transport demand in metropolitan areas is projected to double by 2050, underscoring the critical need for sustainable transportation strategies, forecasting, and planning within an urban context. One challenge in this endeavor is the insufficient attention given to the distribution of vehicle grades within the overall car population during GHG impact assessment, despite the recognized importance of car grade by researchers and policymakers. In this study, we incorporate both engine type and vehicle grade distribution, derived from data on over 30,000 passenger cars registered with the Land Transport Authority of Singapore between 2018 and 2022, into our life cycle assessment analysis. This approach ensures that our scenario-based studies on prediction and planning are more comprehensive than those that consider only the most popular models. Furthermore, while Singapore benefits from a relatively clean electricity supply, the current limitations in charging infrastructure negatively affect the GHG impact of electric vehicles. The insights from this research will inform the development of transportation policies for metropolises like Singapore, contributing to the advancement of a more sustainable transportation system.

Research Progress in the Design Concept of Green Commuting Cities

Abstract: The connection between green commuting and urban design was intimately intertwined in this study. As urbanization progressed, issues such as population growth, housing, transportation, and the environment had become increasingly salient. Green commuting had emerged as a vital strategy to mitigate these challenges and enhance the quality of life for urban residents. This paper elucidated the principles underlying green commuting, highlighting a human-centric approach, the interconnections between public transit, walking, and cycling as modes of travel, and the significance of the digital city paradigm. By examining the transportation systems and urban design methodologies, techniques, and characteristics of Vienna and Copenhagen, the paper delineated the strengths and weaknesses in the development of these cities. It delved into the design approaches and philosophies in green commuting from these two cities that were exemplary and worthy of emulation. The paper proposed potential strategies for green and low-carbon design in future urban transportation, aiming to construct a more ecologically sustainable urban transportation system and infuse new dynamism into urban development.

Electro-mobility – an Opportunity to Develop a More Sustainable Transport System

The future of transport is electro-mobility, a technology that ensures environmentally friendly and energy efficient vehicles on the road. But to exploit the full benefits of electric propulsion there are still many challenges to overcome. These challenges are related to the cost and residual value (aftermarket) of the vehicles, battery life and cost, charging and range. This article discusses these challenges as well as the state of the electric vehicle market. The aim is to present to what extent the development of electric mobility in Europe and in Bulgaria will outline a more sustainable transport system and to outline what the opportunities for its development are. Decarburization of transport is key to achieving sustainable mobility ensuring transport connectivity across the continent.

Understanding the interplay among urban public transport modes: Spatial variation and built environment effects

Research on public transport systems is continually driven by the societal benefits of transport services. However, the nuanced effects of the built environment on public transport mode shares across different modes have received little attention. Using multi-source travel data from Shenzhen, this study presents an analytical framework, which integrates the light gradient boosting machine and Shapley additive explanations, to delve into the complex interplay among public transport modes. The results reveal that most origin-destination pairs with available metro services exhibit a metro share exceeding 50%, underscoring the high attractiveness of the metro. Travel distance emerges as the primary determinant of mode share, with transport-related characteristics proving more influential than land use modifications. This study identifies nonlinear effects of the built environment on mode share, with specific thresholds for built environment characteristics suggesting fine-tuned planning strategies. Metro station densities of 2 counts/km2 correlates with increased metro share, while bus stop densities of 15 counts/km2 are associated with higher bus share. Areas within 2 km of a metro station or with bus stop densities below 15 counts/km2 tend to have higher bike shares. Specific land use ratios and high levels of land use mix are linked to increased shares of certain modes. The findings suggest optimizing bus services in relation to metro availability and offer guidance for balanced planning in promoting metro and shared mobility to achieve a sustainable transport system.

Research on Longitudinal Control of Electric Vehicle Platoons Based on Robust UKF–MPC

In a V2V communication environment, the control of electric vehicle platoons faces issues such as random communication delays, packet loss, and external disturbances, which affect sustainable transportation systems. In order to solve these problems and promote the development of sustainable transportation, a longitudinal control algorithm for the platoon based on robust Unscented Kalman Filter (UKF) and Model Predictive Control (MPC) is designed. First, a longitudinal kinematic model of the vehicle platoon is constructed, and discrete state–space equations are established. The robust UKF algorithm is derived by enhancing the UKF algorithm with Huber-M estimation. This enhanced algorithm is then used to estimate the state information of the leading vehicle. Based on the vehicle state information obtained from the robust UKF estimation, feedback correction and compensation are added to the MPC algorithm to design the robust UKF–MPC longitudinal controller. Finally, the effectiveness of the proposed controller is verified through CarSim/Simulink joint simulation. The simulation results show that in the presence of communication delay and data loss, the robust UKF–MPC controller outperforms the MPC and UKF–MPC controllers in terms of MSE and IAE metrics for vehicle spacing error and acceleration tracking error and exhibits stronger robustness and stability.

Health enhancement through activity travel participation and physical activity intensity

IntroductionPast studies investigate the impact of health parameters on activity and transport options. However, limited research has been done on how activity-travel participation influences health outcomes. Physical activity has the potential to improve health characteristics; however, its intensity (duration and frequency) and optimal mode are yet to be determined. Therefore, the current study aims to use physical activity intensity (PAI) as an intermediate variable to study the correlation among daily activities, transport mode choice, and health parameters to pursue sustainable transportation and improve health parameters. MethodsIn September 2013, a multidimensional three-week household survey was used to collect the data containing 191 households and 732 individuals representing 0.029% of the total population of Bandung, Indonesia. SPSS was used for the data normalization and descriptive analysis, whereas R was used for the multilevel regression and structural equation modeling (SEM). Four models were developed between endogenous, mediator, and exogenous variables. ResultsA significant correlation was found between daily activities, transport mode choice, and health parameters with exceptionally acceptable R2 and p-values. The appraised results indicate that strenuous PAI is 2.1% and 0.3% positively correlates with physical health (PH) and social health (SH), showing that it mediates the correlation among transport-related daily activities and health parameters. High-income households are dependent on motorized transport which is 0.1% and 0.2% negatively associated with PH and SH. Whereas, active transport is 2.0% and 0.2% and public transport is 0.2% and 1.5% positively associated with PH and SH. Replacing short car trips with a sustainable transportation system enhances health parameters and reduces GHG emissions from the transportation sector. ConclusionThe current study concluded that the association was mediated by transport-related PAI which enhances health parameters. Policymakers and practitioners promote transport-related physical activity which may help to enhance health parameters and provide a sustainable transportation system.

Exploring spatial heterogeneity of e-scooter’s relationship with ridesourcing using explainable machine learning

The expansion of e-scooter sharing system has introduced several novel interactions within the existing transportation system. However, few studies have explored how spatial contexts influence these interactions. To fill this gap, this study explored the spatial heterogeneity in e-scooter’s relationship with ridesourcing using data from Chicago, IL. We developed a Light Gradient Boosting Machine (LightGBM) to estimate e-scooter sharing usage using ridesourcing trips along with associated built environment and socio-demographic variables. The model was interpreted using SHapley Additive exPlanations (SHAP). Results indicated that the threshold effects, where the positive relationship between e-scooter sharing and ridesourcing significantly weakened beyond a certain value, were more pronounced in areas with lower population density, fewer jobs, and fewer young, highly educated population. This is primarily attributed to the limited competitiveness of e-scooter sharing in these areas. These findings can assist cities in harmonizing e-scooter sharing and ridesourcing thus promoting sustainable transportation systems.

A Memory-augmented Conditional Neural Process model for traffic prediction

This paper presents the first neural process-based model for traffic prediction, the Memory-augmented Conditional Neural Process (MemCNP). Spatio-temporal traffic prediction involves predicting future traffic patterns based on historical traffic data and the road network structure. This problem remains a challenge due to the dynamic and heterogeneous nature of urban traffic. Existing models often struggle to capture these complexities, particularly in data-limited scenarios. To address these limitations, our model presents a novel framework for uncertainty estimation based on the conditional neural process, and further incorporates a memory network module designed to acquire a representative contextual reference, thereby improving model performance under complex data distributions. By integrating the conditional neural process and the memory network, MemCNP enables the learning of the most representative contexts through iterative updates, enhancing the model’s generalisability. This allows our model to be applicable beyond car traffic, effectively handling diverse real-world traffic scenarios, including urban non-motorised traffic such as cycling, which is essential for advancing more sustainable transportation systems. This is demonstrated by comprehensive experimental results on six benchmark datasets (PeMS04, PeMS07, PeMS08, NYCTaxi, CHIBike, and T-Drive) against existing state-of-the-art traffic prediction models, where MemCNP demonstrates superior performance. Additionally, through ablation and reliability studies, we provide a comprehensive analysis of the model’s effectiveness.

Electrifying the bus network with trolleybus: Analyzing the in motion charging technology

Currently, electric buses are becoming more and more popular, and their number in operation is increasing. The range of electric buses is also increasing and solutions that seem to be working almost without fixed infrastructure are being promised. However, this requires the use of high-capacity batteries, which increases the weight and price of the vehicle and causes high costs of battery replacement during operation. Moreover, if we take into account the growing demand for batteries, limited raw material resources, and the environmental impact of the battery production process, the optimization of battery capacity in vehicles may turn out to be a key issue. In this light, trolleybus becomes a sustainable and economically efficient bus electrification technology, if considered in an international scope and a medium- to long-term approach. The article provides a comprehensive study of challenges and potential solutions related to electric buses, which covers the theoretical analysis, technical aspects and practical applications, thus making a valuable resource for readers interested in sustainable urban transport systems. It presents the trolleybus technology, especially with modern solutions, as a sustainable and economically efficient tool for bus electrification. The article shows that the In Motion Charging (IMC) system reduces the need for high-capacity batteries under 100 kWh, which allows to extend their service life up to 15 years and, consequently, to reduce the number of buses needed for operation. The research was based on real measurement data from the transport system in Gdynia (Poland).

ENVIRONMENTAL PLANNING AND DESIGN: EXPLORING URBAN RESILIENCE THROUGH E-HAILING

Sustainable cities strive for balance in environmental health, economic vitality and social equity through efficient and resilient urban planning. Crucial to this balance, sustainable transportation systems reduce carbon emissions, ease traffic congestion and promote clean energy. Among various sustainable transportation options, e-hailing has gained global popularity, offering convenient rides through smartphone apps. This quantitative research focuses on exploring the usage patterns of e-hailing services among university students at the main campus of Universiti Sains Malaysia. A total of 392 university students were surveyed using stratified random sampling, and SPSS analysis revealed a moderate positive correlation between attitudes towards e-hailing and satisfaction with safety, price, convenience and availability. Furthermore, regression analysis confirmed significant relationships between attitude and satisfaction levels. By investigating how travel behaviour patterns and attitudes towards e-hailing influence student satisfaction, the study aims to understand e-hailing’s role as an alternative mode of transportation in enhancing overall transportation service satisfaction. These insights offer valuable guidance for transport planners, e-hailing companies and university administrations seeking to improve transportation options and student satisfaction.

Income moderates the nonlinear influence of built environment attributes on travel-related carbon emissions

Policymakers have adopted built environment policies to modify people's travel behavior and the related emissions. However, few studies have examined the interactive impact between income level and built environment attributes on travel-related carbon emissions (TCE), and only several studies consider their nonlinear relationships. With data from the Twin Cities, US, this study estimated the nonlinear effects of built environment attributes and demographics on TCE. It further examined the interactive impacts between household income and built environment attributes. The findings highlight that demographics exert a greater influence on TCE than the built environment. Employment status, job accessibility, and gender are the most important predictors. Besides individual nonlinear relationships, household income and built environment attributes have salient interactive impacts on TCE. The results suggest that providing environment friendly and affordable transportation choices to low-income population, switching to clean energy vehicles, and offering more matched job opportunities to low-income population near their residence are promising to create a sustainable transportation system.

Artificial Intelligence-Based Adaptive Traffic Signal Control System: A Comprehensive Review

The exponential increase in vehicles, quick urbanization, and rising demand for transportation are straining the world’s road infrastructure today. To have a sustainable transportation system with dynamic traffic volume, an Adaptive Traffic Signal Control system (ATSC) should be contemplated to reduce urban traffic congestion and, thus, help reduce the carbon footprints/emissions of greenhouse gases. With dynamic cleave, the ATSC system can adapt the signal timing settings in real-time according to seasonal and short-term variations in traffic demand, enhancing the effectiveness of traffic operations on urban road networks. This paper provides a comprehensive study on the insights, technical lineaments, and status of various research work in ATSC. In this paper, the ATSC is categorized based on several road intersections (RIs), viz., single-intersection (SI) and multiple-intersection (MI) techniques, viz., Fuzzy Logic (FL), Metaheuristic (MH), Dynamic Programming (DP), Reinforcement Learning (RL), Deep Reinforcement Learning (DRL), and hybrids used for developing Traffic Signal Control (TSC) systems. The findings from this review demonstrate that modern ATSC systems designed using various techniques offer substantial improvements in managing the dynamic density of the traffic flow. There is still a lot of scope to research by increasing the number of RIs while designing the ATSC system to suit real-life applications.

Motorcycle Recognition System Using Convolutional Neural Network

Vehicle detection is becoming increasingly important for highway management, particularly in Aklan, where motorcycles and tricycles are the predominant modes of transportation. Due to their diverse designs, accurate detection of these vehicles remains challenging. This study addresses this issue by developing a vision-based motorcycle recognition system using a Convolutional Neural Network (CNN) implemented in MATLAB. A new high-definition dataset, comprising 34,002 annotated instances from 17,785 motorcycle images and 16,217 tricycle images, was created. The dataset was collected from various locations around Ibajay, Aklan, using regular cameras and smartphones. The images were classified into motorcycles and tricycles and then used to train the CNN model, which was compared with the You Only Look Once (YOLO) version 2 network. Experimental results indicate that the proposed CNN-based motorcycle recognition system achieves high detection accuracy, comparable to the YOLOv2 model. This system contributes to Sustainable Development Goal (SDG) 11 by improving sustainable urban transportation systems and traffic management. Additionally, it holds significant commercial value for motorcycle brands through enhanced vehicle tracking and market analysis.

Workforce development in the transport sector amidst environmental change: A conceptual review

This study embarks on an exploratory journey to elucidate the complex interplay between workforce development and environmental sustainability within the transport sector. It underscores the critical importance of nurturing a skilled workforce capable of propelling the sector towards sustainable practices, thereby addressing pressing environmental challenges. To achieve a comprehensive understanding, this review synthesizes a wide array of data sources, adopting a strategic framework for literature search and analysis. The methodology hinges on stringent criteria for literature selection, ensuring the relevance and quality of the information reviewed. Central to the study is the development of a theoretical framework that situates workforce development within the paradigm of sustainable transport. This includes an examination of the architectural underpinnings of sustainable transport systems, the potential for modal shifts to lessen environmental impacts, and the pivotal role of technological and methodological advancements. Moreover, the review highlights innovative practices in workforce training and development that are crucial for meeting future demands. The in-depth analysis sheds light on the multifaceted impacts of workforce development initiatives on achieving sustainable transport objectives, emphasizing the economic, social, and environmental dimensions. It identifies existing gaps in workforce competencies and outlines strategies for skill enhancement. Additionally, the study emphasizes the indispensable role of policy, standards, and stakeholder collaboration in fostering sustainable transport. Conclusively, the study presents strategic recommendations for policymakers, educators, and industry leaders, charting a course for a resilient and sustainable transport workforce. It posits that an integrative approach, marrying workforce development with environmental sustainability, is essential for navigating the future challenges and opportunities within the transport sector.

Experimental and numerical studies on performance investigation of a diesel engine converted to run on LPG

With the growing environmental concerns and depletion of energy resources, considerable efforts are focused on developing ecofriendly and sustainable transportation systems. This includes the conversion of outdated diesel engines into ecofriendly liquefied petroleum gas (LPG) engines. In this study, a Euro-6 diesel engine mounted on a commercial vehicle (mega truck) was converted into a propane LPG engine. The combustion chamber was modified by changing the piston shapes (PSs), compression ratios (CRs), ignition system, throttle body, and injector design to enable operation using propane. The performance of the modified engines with two different PSs, PS1 and PS2, were experimentally investigated. In terms of the power, the engine designed using PS2 demonstrated better performance than PS1 under all operating speeds, except 2600 rpm. Particularly, PS2 outperformed PS1 by more than 10 Nm in the low-speed range of 1000–1600 rpm. Under full load, the PS2-equipped LPG engine achieved the maximum torque and power of 834 Nm at 1600 rpm and 170 kW at 2600 rpm, achieving 106 % and 86 % of the target torque and power, respectively. Additional exhaust gas emission tests were performed using the PS2-equipped LPG engine. The engine performance varied with the application of exhaust-gas recirculation. Moreover, the results of the world-harmonized stationary cycle mode tests confirmed the conformance of the modified LPG engine to all Euro-6 emission standards. Furthermore, simulation-based performance optimization was conducted considering two PSs and four CRs. Based on the simulations, PS1 achieved higher engine performance and lower CO2 and soot emissions, whereas PS2 had lower NO emissions. Additionally, increasing the CR improved the performance and reduced the CO2 and soot emissions for both PSs. These results indicate the possibility of developing LPG engines with performance comparable to diesel engines. In particular, the modified LPG engine proposed in this work may be installed on buses and trucks in the future and is anticipated to replace outdated medium-generator and marine engines.

Project approach to control system creating for modernizing traffic control at territorial communities’ reconstruction

The reform of urban planning activities has opened up opportunities to apply a modern approach to the strategic planning of territorial community development by developing and implementing the Concept of Integrated Territorial Development (CITD) of the territorial community, as well as sectoral plans for the implementation of specific CITD priorities. One of these priorities is the creation of a sustainable transportation system for the community that provides residents with high-quality transportation services, minimizes environmental harm, and ensures long-term socio-economic benefits. To achieve this, a Sustainable Urban Mobility Plan (SUMP) is developed and implemented, which includes, among other things, the modernization of traffic control. The full-scale military aggression against Ukraine has necessitated a reevaluation of the concept of sustainable mobility and the implementation of measures to adapt it to the requirements of military and post-war community recovery. The focus is now on the resilience of territorial communities to critical situations (shock events) related to military actions and ongoing threats from the aggressor. Particular attention must be given to large cities, which represent one of the strategic targets for the aggressor. The proposed article examines the peculiarities of implementing a traffic control management modernization project for the military and the post-war recovery of a territorial community in accordance with the principles of sustainable mobility. It justifies the need to develop a version of the SUMP that includes scenarios for the development of sustainable mobility tailored to the requirements of military and post-war recovery. Three main directions are proposed for the implementation of the road traffic management system's responsibilities in developing the transportation system of the territorial community: soft measures, hard measures, and measures to enhance the transportation resilience of the territorial community. The study concludes that there is a need to develop new indicators that characterize the transportation resilience of territorial communities. conducting a SWOT analysis of the strengths and weaknesses of communities and their mobility, considering the impacts of military aggression; adjusting existing SUMPs and developing new ones, particularly for large cities and border communities; adjusting the composition, structure, and functions of the traffic management system to ensure effective current mobility management in response to existing challenges and threats; implementing and providing technical support for the execution of traffic control schemes and traffic signal timing plans according to scenarios that address critical moments (shock events); applying methodologies for managing territorial infrastructure projects with a focus on digitizing traffic control processes.

Analyzing Energy Efficiency and Battery Supervision in Electric Bus Integration for Improved Urban Transport Sustainability

Addressing the critical challenge of reducing local emissions through the electrification of urban public transport, this research specifically focuses on integrating electric buses. The primary objectives are to evaluate energy efficiency and ensure battery cell supervision. Introducing electric buses plays a significant role in reducing emissions, contributing to more sustainable urban transport systems. However, this transition introduces a set of new challenges, including the complexities of electric charging logistics, the establishment of new consumption standards, and the intricate relationships between distance traveled, ambient temperature, passenger load, and battery health. Methodologically, this study collects and examines factors impacting energy consumption, including external temperatures, bus conditions, road conditions, and driver behavior. By analyzing these variables, a baseline for actual consumption can be established, allowing for the calculation of an energy balance to identify energy inefficiencies. This enables the optimization of route planning, the strategic selection of stops, and the efficient scheduling of charging times, along with ensuring the proper scaling of the bus battery system. This study found that energy consumption peaked at 116.73 kWh/100 km in the lowest temperature range of −5 °C to 0 °C. Consumption decreased significantly with rising temperatures, dropping by 25 kWh between 5 °C and 10 °C and by an additional 10 kWh between 10 °C and 15 °C. Beyond 20 °C, variations were more influenced by route and driving style than by temperature. Route and driver variability significantly influenced energy consumption, with up to threefold differences across routes due to factors such as road type and traffic volume. Additionally, there was a 31.85% difference between the most and least efficient drivers, highlighting the critical impact of driving style. Furthermore, this study explores the assessment of battery systems through cell-level diagnostics to detect potential faults. Considering that buses are equipped with significantly more batteries than typical electric vehicles, detecting and localizing faults at the cell level is crucial to avoid the substantial costs and environmental impact associated with replacing large battery systems. Utilizing the results of this research and the applied examination methods, it is possible to enhance energy efficiency and extend battery life, thereby contributing to the development of more sustainable and cost-effective urban transport solutions.

Energy management optimization of hybrid electric vehicles based on deep learning model predictive control

In this paper, the hybrid electric vehicle (HEV) energy management optimization method is proposed based on deep learning (DL) model predictive control. Through empirical research combined with the questionnaire survey, this article not only provides a new perspective and practical basis but also improves the efficiency and accuracy of the model by improving the relevant algorithms. The study first analyzes the importance of HEV energy management and reviews the existing literature. Then, the optimization method of HEV energy management based on the deep learning model is introduced in detail, including the composition of energy management for hybrid electric vehicles, the structure and working principle of the deep learning model, especially the backpropagation neural network (BPNN) and the convolutional neural network (CNN), and the steps of application of deep learning in energy management. In the experimental part, questionnaire data from 1,500 consumers were used to design the HEV energy management optimization scheme, and consumers’ attitudes and preferences towards HEV energy optimization were discussed. The experimental results show that the proposed model can predict HEV energy consumption under different road conditions (urban roads, highways, mountain areas, suburban areas, and construction sites), and the difference between the average predicted energy consumption and the actual energy consumption is between 0.1KWH and 0.3KWH, showing high prediction accuracy. In addition, the deep learning-based energy management strategy outperforms traditional control strategies in terms of fuel consumption (6.2 L/100 km), battery charge and discharge times (814), battery life, and CO2 emissions, significantly improving the efficiency of HEV energy. These results demonstrate the great potential and practical application value of deep learning models in the optimization of energy management of HEVs, helping to drive the development of more sustainable and efficient transportation systems.

Investigating Factors Influencing Crash Severity on Mountainous Two-Lane Roads: Machine Learning Versus Statistical Models

Due to poor road design, challenging terrain, and difficult geological conditions, traffic accidents on mountainous two-lane roads are more frequent and severe. This study aims to address the lack of understanding of key factors affecting accident severity with the goal of improving mountainous traffic safety, thereby contributing to sustainable transportation systems. The focus of this study is to compare the interpretability of model performances with three statistical models (Ordered Logit, Partial Proportional Odds Model, and Multinomial Logit) and six machine learning models (Decision Tree, Random Forest, Gradient Boosting, Extra Trees, AdaBoost, and XGBoost) on two-lane mountain roads in Yunnan Province, China. Additionally, we assessed the ability of these models to uncover underlying causal relationships, particularly how accident causes affect severity. Using the SHapley Additive exPlanations (SHAP) method, we interpreted the influence of risk factors in the machine learning models. Our findings indicate that machine learning models, especially XGBoost, outperform statistical models in predicting accident severity. The results highlight that accident patterns are the most significant determinants of severity, followed by road-related factors and the type of colliding vehicles. Environmental factors like weather, however, have minimal impact. Notably, vehicle falling, head-on collisions, and longitudinal slope sections are linked to more severe accidents, while minor accidents are more frequent on horizontal curve sections and areas that combine curves and slopes. These insights can help traffic management agencies develop targeted measures to reduce accident rates and enhance road safety, which is critical for promoting sustainable transportation in mountainous regions.

Toward A Comprehensive Framework for Accessibility Measures for Movement-Challenged Persons

Access is one of the most important rights for persons with disabilities (PWDs), particularly for movement-challenged persons (MCPs). To adopt appropriate policy measures that ensure this right for MCPs, it is necessary to evaluate accessibility appropriately. There are, however, very few accessibility measures applicable to MCPs. The current study proposes a framework for a novel comprehensive accessibility measure for MCPs (CAMMCP) by integrating parameters of universal design and the impact of mode-specific challenges faced by MCPs with existing accessibility measures following an origin-to-destination approach. The study presents a literature review of existing accessibility measures and suggests approaches to modify measures to suit mobility challenges faced by MCPs. Modified accessibility measures have been integrated with other parameters related to universal design and mode-specific problems. Universal-design-related parameters will include ramp slope and curb cuts, which can be measured through physical survey. Information on the impact of mode-specific challenges to MCP accessibility can be collected through questionnaires and participatory survey methods. The study also suggests approaches to modifying existing accessibility measures that do not differentiate between mobility constraints for MCPs and non-MCPs when evaluating accessibility. CAMMCP will be helpful for transportation policymakers taking the initiative to improve built-environment accessibility for MCPs as well as for evaluating projects through the lens of transport equity. It can be particularly helpful in determining walkability and transit-service accessibility. Hopefully, it will help build better road networks and transportation modes to develop a sustainable and inclusive transportation system.