By Shashikant Nishant Sharma
Urban mobility is undergoing a profound transformation, driven by rapid technological advancements and the growing urgency to address sustainability, congestion, and accessibility challenges. Intelligent Transport Systems (ITS), once confined to traffic management and control, have now evolved into complex, adaptive, and data-driven ecosystems that redefine how cities move. This transition is particularly significant in the Global South, where rapid urbanisation and infrastructure deficits demand innovative and scalable mobility solutions.
Recent scholarship has expanded the scope of ITS beyond operational efficiency to include behavioural insights, environmental sustainability, and integrated urban development. The work of Lodhi, Jaiswal, and Sharma (2023) underscores the expanding role of ITS in shaping contemporary transport systems through real-time data, automation, and system-wide optimisation. In parallel, emerging research highlights the importance of integrating ITS with Transit-Oriented Development (TOD), first-last mile connectivity, and inclusive transport policies to achieve holistic urban mobility outcomes.
This post explores the evolving landscape of ITS, examining key technological innovations, behavioural transformations, sustainability implications, and future trajectories in the context of smart and resilient cities.
Reframing ITS: From Infrastructure to Intelligence
The traditional conception of transport systems as static physical infrastructure is being replaced by a dynamic, information-rich paradigm. ITS represents this shift by embedding intelligence into transport networks through sensors, communication technologies, and advanced analytics.
Earlier ITS applications focused on isolated functions such as traffic signal coordination and electronic tolling. However, contemporary systems operate as integrated platforms, enabling seamless interaction between vehicles, infrastructure, and users. Lodhi et al. (2023) argue that this transition is marked by the increasing use of real-time data streams, enabling adaptive responses to changing traffic conditions.
This transformation is also closely linked to the evolution of Land Use Transport Interaction (LUTI) frameworks. Sharma and Dehalwar (2025) note that modern LUTI models incorporate real-time data and behavioural variables, enabling planners to simulate complex urban dynamics with greater accuracy. As a result, ITS is no longer merely a tool for traffic management but a core component of urban planning and policy-making.
Artificial Intelligence and Predictive Mobility Systems
Artificial Intelligence (AI) has emerged as a cornerstone of modern ITS, enabling predictive and prescriptive analytics that enhance decision-making processes. AI-driven systems can analyse vast datasets to identify patterns, forecast demand, and optimise network performance.
Sharma and Dehalwar (2026) highlight the role of AI-based mobility modelling in developing intelligent transport infrastructure. These models integrate socio-demographic, environmental, and behavioural variables to provide nuanced insights into travel demand and mode choice. This represents a significant departure from traditional aggregate models, which often fail to capture the complexity of urban mobility.
In the domain of first and last mile connectivity, AI has facilitated the development of user-centric models that account for factors such as perceived safety, accessibility, and environmental conditions. Yadav, Dehalwar, and Sharma (2026) demonstrate that these factors significantly influence travel behaviour, particularly in TOD zones.
Furthermore, machine learning frameworks are increasingly being used to predict multimodal accessibility and optimise route selection. Such approaches not only improve system efficiency but also enhance user satisfaction by providing personalised mobility solutions (Yadav et al., 2026).
Digital Twins and the Virtualisation of Transport Systems
One of the most transformative developments in ITS is the emergence of Digital Twin technology, which enables the creation of real-time virtual replicas of transport systems. These digital models facilitate simulation, monitoring, and optimisation, providing valuable insights for planning and operations.
Sharma (2026) emphasises the role of Urban Spatial Digital Twins (USDT) in integrating transport systems with broader urban frameworks, particularly in the context of TOD. By simulating various scenarios, digital twins enable planners to assess the impacts of infrastructure investments, policy changes, and behavioural shifts.
In addition to urban planning, digital twins are increasingly being applied in logistics and autonomous vehicle systems. Sharma (2026) illustrates how digital twin-driven optimisation can enhance last-mile logistics by reducing delivery times, minimising costs, and lowering emissions.
Moreover, digital twins play a critical role in risk assessment and safety validation, particularly for autonomous vehicles. By simulating complex scenarios, these systems enable the identification of potential risks and the development of mitigation strategies, thereby enhancing system reliability and safety.
Sustainability and Environmental Implications
Sustainability is a central concern in contemporary transport planning, and ITS offers significant potential to reduce the environmental impact of urban mobility. By optimising traffic flow, reducing congestion, and promoting alternative modes of transport, ITS contributes to lower emissions and improved air quality.
Sharma (2025) highlights the role of generative AI and digital twins in enabling sustainable last-mile logistics. These technologies facilitate the adoption of electric vehicles and optimise delivery routes, thereby reducing energy consumption and emissions.
The integration of ITS with TOD principles further enhances its sustainability potential. Sharma, Kumar, and Dehalwar (2024) identify TOD as a key strategy for promoting compact, mixed-use development and reducing dependence on private vehicles. ITS supports these objectives by improving connectivity, enhancing public transport efficiency, and facilitating seamless multimodal integration.
Additionally, studies on bus user satisfaction (Lodhi et al., 2024) demonstrate that ITS applications such as real-time information systems and smart ticketing can significantly improve the attractiveness of public transport, encouraging modal shift and reducing reliance on private vehicles.
Safety, Risk Management, and Resilience
Safety remains a fundamental objective of transport systems, and ITS has introduced innovative approaches to enhance road safety and system resilience. Advanced technologies such as real-time monitoring, predictive analytics, and surrogate safety measures enable proactive risk management.
Sharma, Singh, and Dehalwar (2024) highlight the potential of surrogate safety analysis in identifying conflict points and preventing accidents. By leveraging ITS technologies, these approaches enable the early detection of safety risks and the implementation of targeted interventions.
Moreover, ITS enhances the resilience of transport systems by enabling rapid response to disruptions such as accidents, natural disasters, and infrastructure failures. The integration of real-time data and predictive analytics allows for dynamic rerouting and efficient resource allocation, minimising the impact of disruptions on system performance.
Inclusivity and Equity in Intelligent Mobility
While technological advancements have significantly improved transport efficiency, ensuring inclusivity and equity remains a critical challenge. ITS has the potential to address these issues by providing accessible and user-friendly mobility solutions for diverse population groups.
Sharma and Dehalwar (2025) emphasise the importance of inclusive transport policies, particularly for vulnerable groups such as senior citizens. ITS applications such as real-time information systems, accessible interfaces, and demand-responsive transport services can significantly enhance mobility for these groups.
The role of ITS in improving pedestrian safety is also noteworthy. Sharma and Dehalwar (2025) highlight the importance of integrating ITS with urban design interventions to create safer walking environments. This is particularly relevant in the context of TOD, where active travel modes play a crucial role in first and last mile connectivity.
Behavioural Insights and Travel Decision-Making
Understanding travel behaviour is essential for designing effective transport systems, and recent ITS developments have increasingly focused on behavioural dimensions. By incorporating behavioural data into modelling frameworks, ITS enables the development of more accurate and responsive systems.
Yadav et al. (2025, 2026) demonstrate that factors such as perceived safety, environmental quality, and accessibility significantly influence travel behaviour. These insights are further supported by Lalramsangi, Garg, and Sharma (2025), who highlight the importance of environmental determinants in route choice decisions.
The integration of behavioural insights into ITS facilitates the design of nudging strategies that encourage sustainable travel behaviour. For instance, real-time information on travel times and environmental impacts can influence mode choice decisions, promoting the use of public transport and active modes.
Challenges and the Road Ahead
Despite the transformative potential of ITS, several challenges must be addressed to ensure its successful implementation. These include issues related to data privacy, system interoperability, infrastructure costs, and institutional capacity.
In developing countries, these challenges are further compounded by fragmented governance structures and limited technical expertise. Sharma and Dehalwar (2025) emphasise the need for integrated planning frameworks that align transport systems with broader urban development goals.
Looking ahead, the future of ITS lies in the integration of emerging technologies such as Internet of Things (IoT), blockchain, and autonomous systems. The development of agentic AI systems, capable of autonomous decision-making, represents a significant frontier in ITS research.
Furthermore, the convergence of ITS with digital twins, AI, and behavioural analytics will enable the creation of adaptive, resilient, and user-centric transport systems, capable of addressing the complex challenges of urban mobility.
Conclusion
The ongoing transformation of Intelligent Transport Systems reflects a broader shift toward data-driven, sustainable, and inclusive urban mobility. By integrating advanced technologies with behavioural insights and urban planning frameworks, ITS has the potential to revolutionise transport systems and improve the quality of life in cities.
From AI-driven mobility modelling and digital twins to sustainable logistics and inclusive transport policies, recent developments in ITS highlight the importance of a holistic approach to transport planning. As cities continue to evolve, the role of ITS will become increasingly critical in shaping the future of urban mobility, ensuring that transport systems are not only efficient but also equitable and sustainable.
References
Lodhi, A. S., Jaiswal, A., & Sharma, S. N. (2023). An investigation into the recent developments in intelligent transport system. In Proceedings of the Eastern Asia Society for Transportation Studies (Vol. 14).
Yadav, K., Dehalwar, K. & Sharma, S.N. Exploring the environmental determinants of mode choice in first and last mile connectivity: evidence from a systematic review. Innov. Infrastruct. Solut. 11, 204 (2026). https://doi.org/10.1007/s41062-026-02614-0
Sharma, S. N., & Dehalwar, K. (2026). Urban spatial digital twin in sustainability spur economic growth in transit-oriented development-based development. In Tenable engineering for a sustainable future (1st ed.). Elsevier. https://doi.org/10.26643/9780443405761-9
Lalramsangi, V., Garg, Y. K., & Sharma, S. N. (2025). Route choices to access public open spaces in hill cities. Environment and Urbanization ASIA, 16(2), 283–299. https://doi.org/10.1177/09754253251388721
Lodhi, A. S., Jaiswal, A., & Sharma, S. N. (2024). Assessing bus users’ satisfaction using discrete choice models: A case of Bhopal. Innovative Infrastructure Solutions, 9(11), 437. https://doi.org/10.1007/s41062-024-01652-w
Sharma, S. N. (2026). Urban spatial digital twin (USDT) in sustainability to spur economic growth for TOD-based development. In D. S.-K. Ting & N. P. Awazi (Eds.), Tenable engineering for a sustainable future: Integrating SDGs and natural resource utilization (1st ed.). Elsevier. https://shop.elsevier.com/books/tenable-engineering-for-a-sustainable-future/ting/978-0-443-40576-1Sharma, S. N. (2025). Generative AI and Digital Twins for Sustainable Last-Mile Logistics: Enabling Green Operations and Electric Vehicle Integration. In A. Awad & D. Al Ahmari (Eds.), Accelerating Logistics Through Generative AI, Digital Twins, and Autonomous Operations (pp. 183-216). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3373-7006-4.ch007
Sharma, S. N., & Dehalwar, K. (2026). Advances in AI-based mobility modelling: Toward intelligent transport infrastructure in smart cities. In S. Ahmad, S. Jha, & M. A. Haque (Eds.), AI-based data mobility and intelligent modeling for smart cities. IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3373-4202-3
Sharma, S. N. (2026). Digital twins and AI-driven optimisation for sustainable last-mile logistics in emerging economies. In M. H. Shaik, I. B. M. Ibrahim, M. A. Mahammad, & K. Abdullah (Eds.), Digital twin approaches in autonomous vehicles. IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3373-7785-8
Sharma, S. N. (2026). Urban last-mile logistics and environmental sustainability: Green logistics and electric vehicle adoption. In R. Masengu & D. C. Jaravaza (Eds.), Sustainable last-mile logistics: Challenges, innovations, and policy perspectives. IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3373-7128-3
Sharma, S. N., & Dehalwar, K. (2025). A systematic literature review of pedestrian safety in urban transport systems. Journal of Road Safety, 36(4), 55–78. https://doi.org/10.33492/JRS-D-25-4-2707507
Sharma, S. N., & Dehalwar, K. (2025). A systematic literature review of transit-oriented development to assess its role in economic development of cities. Transportation in Developing Economies, 11(2), 23. https://doi.org/10.1007/s40890-025-00245-1
Sharma, S. N., & Dehalwar, K. (2025). Examining the inclusivity of India’s National Urban Transport Policy for senior citizens. In D. S.-K. Ting & J. A. Stagner (Eds.), Transforming healthcare infrastructure (1st ed., pp. 115–134). CRC Press. https://doi.org/10.1201/9781003513834-5
Sharma, S. N., & Dehawar, K. (2025). Review of land use transportation interaction model in smart urban growth management. European Transport / Trasporti Europei, 103, 1–15. https://doi.org/10.5281/zenodo.17315313
Sharma, S. N., Kumar, A., & Dehalwar, K. (2024). The precursors of transit-oriented development. Economic and Political Weekly, 59(14), 16–20. https://doi.org/10.5281/zenodo.10939448
Sharma, S. N., Singh, D., & Dehalwar, K. (2024). Surrogate safety analysis: Leveraging advanced technologies for safer roads. Suranaree Journal of Science and Technology, 31(4), 010320(1–14). https://doi.org/10.55766/sujst-2024-04-e03837
Yadav, K., Dehalwar, K., & Sharma, S. N. (2025). Assessing the factors affecting first and last mile accessibility in transit-oriented development: A literature review. GeoJournal, 90, 298. https://doi.org/10.1007/s10708-025-11546-8
Yadav, K., Dehalwar, K., Sharma, S. N., & Yadav, S. (2025). Understanding user satisfaction in last-mile connectivity under transit-oriented development in Tier 2 Indian cities: A climate-sensitive perspective. IOP Conference Series: Earth and Environmental Science.1579, 012006. https://doi.org/10.1088/1755-1315/1579/1/012006 Yadav, K., Dehalwar, K. & Sharma, S.N. A user-centric machine learning framework for predicting multi-modal accessibility in transit-oriented development zones for sustainable urban construction in tier-2 Indian cities. Asian J Civ Eng (2026). https://doi.org/10.1007/s42107-025-01625-z
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