Journal of Transport and Land Use

Artificial intelligence (AI) is transforming how people, goods, and information move across both physical and virtual landscapes. Emerging AI-driven mobilities, such as autonomous vehicles, ultra-fast delivery services, and immersive live commerce, are reshaping transportation systems and the urban spaces they serve. These changes introduce new dynamics and uncertainties in the interactions between urban transportation and land-use patterns.

At the same time, the proliferation of novel data sources and advanced analytical methods present new opportunities to understand how AI is transforming the spatial organization of cities, the design and utilization of transportation infrastructure, and the planning processes that inform urban development. However, they also raise critical questions about mobility, accessibility, resilience and sustainability in increasingly automated and data-rich urban systems. 

The Journal of Transport and Land Use (JTLU) invites original research that critically examines the role of AI in shaping transportation, land use, and their interactions. Submissions should align with the scope of JTLU, emphasizing the integrated nature of transport and land-use systems. Starting March 1, 2026, we welcome submissions that address one of the two thematic areas outlined below. 

Theme 1: Embedded Intelligence in Transport and Land Use Systems

Embedded intelligence refers to AI systems that are integrated into physical infrastructure and mobile technologies such as autonomous vehicles (AVs), delivery drones, and service robots. These technologies are reconfiguring how people move and interact with urban and rural environments. The embedded intelligence theme explores how embedded AI systems influence and respond to land use patterns, accessibility, and travel behavior.

Example Topics

• How embedded AI technologies adapt to various land use contexts (e.g., responsive robotic delivery services for urban and rural settings)
• Whether and how spatial accessibility is enhanced or disrupted by AI technologies (e.g., AVs, unmanned aerial vehicles, and electric vertical take-off and landing aircraft)
• How AVs, delivery drones, and other AI technologies affect travel patterns, trip generation, and land-use allocation
• How urban morphologies affect the deployment and performance of embedded AI technologies (e.g., sidewalk robots vs. drone corridors).
• The co-evolution of intelligent infrastructure (e.g., adaptive traffic lights, smart curbside management systems) and mobility behavior (e.g., AI-driven route selections).
  

Theme 2: Algorithmic AI for Modeling and Planning Urban Mobility and Land Use

This theme focuses on algorithmic AI, including machine learning, large language models (LLMs), world models, and computer vision, as tools for understanding and predicting urban dynamics. The systems are increasingly used to analyze transport networks, forecast land use changes, optimize site selection, and influence  travel demand through digital platforms. The Algorithm AI theme focused on leveraging algorithmic AI for urban analysis and planning, and critically examining its policy implications.

Example Topics

• Application of AI algorithms (e.g., reinforcement learning, optimization) for traffic operations or multimodal planning
• Use of LLMs to generate, interpret, or translate transport and land use policies
• AI-powered optimal site selection for public facilities, retail, transit hubs, or other infrastructure using geospatial and demand data
• Computer vision and world model applications for monitoring and predicting land use dynamics, pedestrian flow, bike usage, curb activity, and other micro-mobility patterns 
• Predictive modeling of land valuation or activity space changes in response to digital platforms or AI-enabled logistics services

Guest Editors

• Haotian Zhong, Associate Professor, Renmin University of China, hzhong@ruc.edu.cn
• Wei Zhai, Associate Professor, University of Texas at Arlington, wei.zhai@uta.edu
• Madison Lore, Assistant Professor, Cornell University, madison.lore@cornell.edu

Timeline

Submission Open: March 1, 2026

Submission Deadline: June 15, 2026

Submission Instruction

We welcome theoretical, empirical, methodological, or applied research that offers new insights into the AI–transport–land use nexus. Submissions should be original research articles; literature review articles and short vision papers are not accepted. There is no strict word limit, but manuscripts typically range between 8,000 to 10,000 words.

Manuscripts should be submitted through the JTLU official website starting March 1, 2026. Authors must clearly indicate that their manuscript is intended for this special issue on AI and the Evolving Dynamics of Transportation and Land Use during the submission process. For detailed submission guidelines, please visit this JTLU page.