Transportation Route Optimization

Regardless of what side of the AI debate you find yourself. Truth is, AI is here, it’s evolving, and it will become a key component of everything we do in the future. You can’t stop evolution. AI presents numerous opportunities to revolutionize public transportation, paving the way for more efficient, sustainable, and user-friendly systems. Here are some of the key opportunities I’m keeping my eyes on: Optimized Routes and Schedules: AI can dynamically adjust routes and schedules based on real-time data, reducing travel times and improving punctuality. Traffic Flow Management: AI can optimize traffic signals and manage congestion, prioritizing public transportation vehicles and enhancing overall traffic flow. Predictive Maintenance: By predicting and addressing maintenance needs before failures occur, AI can reduce repair costs and extend the lifespan of vehicles and infrastructure. Energy Management: AI can optimize energy usage for electric buses and trains, leading to significant cost savings and reduced environmental impact. Real-time Surveillance: AI-powered video analysis can enhance security by detecting suspicious activities and potential threats in real-time. Incident Prediction and Prevention: AI can predict potential accidents or safety issues, allowing for proactive measures to be taken. Personalized Travel Information: AI can provide personalized travel recommendations, real-time updates, and customer support through chatbots and virtual assistants. Seamless Payment Systems: AI can facilitate smart ticketing systems with dynamic pricing and contactless payments, making the payment process smoother for passengers. Smart Resource Allocation: AI can help deploy resources more efficiently, reducing waste and improving the sustainability of transportation networks. Demand Prediction: AI can analyze patterns to forecast future transportation needs, aiding in better planning and resource allocation. Multi-modal Transport Solutions: AI can integrate various modes of transportation (e.g., buses, trains, bikes, ridesharing) into a cohesive system, providing users with seamless end-to-end travel options. Solving the last mile paradigm. Smart City Initiatives: AI in public transportation can be part of broader smart city initiatives, improving overall urban mobility and connectivity. Enhanced Analytics: AI can process vast amounts of data to provide insights and support decision-making processes for transportation authorities and operators. Performance Monitoring: Continuous monitoring and analysis of system performance can lead to ongoing improvements and innovation in public transportation.

While cities grapple with the adverse impacts of climate change, local governments, especially in developing nations, need to rethink the paradigm by which urban mobility and urban planning can be guided more cautiously.  A-S-I, introduced in 1994, is a demand-based mobility planning approach that can assist local governments in achieving significant mobility-related GHG emission reductions, reduced energy consumption, reduced congestion, and ultimately, more livable cities. The essence of the sustainable planning approach is that it focuses on the mobility needs of people rather than car infrastructure and includes 3 pillars, i.e., ▪️Avoid | focuses on improving the efficiency of transport system as a whole via transit-oriented and compact development of thereby cities, reducing the demand for motorized travel. ▪️Shift | focus on a modal shift from energy consuming and polluting mobility modes (cars) to eco-friendly modes, i.e., Active and Public Transport. ▪️Improve | focuses on vehicle and fuel efficiency, introducing renewable energy sources, as well as on the optimization of operational efficiency of the public transport system.  To facilitate the approach, the following key instruments are required to be contextualized: ▪️Planning | land use planning for public transport and NMT ▪️Regulatory | norms/standards and management ▪️Economic | taxation, subsidies and emissions trading ▪️Information | public awareness, marketing and agreements ▪️Investment | cleaner technologies and production Daniel Bongardt | Lena Stiller | Anthea S. | Armin Wagner | Transformative Urban Mobility Initiative | Sustainable Urban Transport Project | SDGs New Urban Agenda | Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH | Federal Ministry for Economic Cooperation and Development (BMZ) Report Link | https://lnkd.in/gkAPG2wM #climatechange #urbanmobility #sustainablemobility #avoid #shift #improve #safe #inclusive #affordable #accessible #activemobility #publictransport #integratedinfrastructure

How can we customize multi-modal travel journey planning while accounting for user's preferences as well as the integration between fixed- and flexible on-demand services? https://lnkd.in/eCkzzU7g We propose a preference-based optimization framework for multi-modal trip planning with public transport, ride-pooling services, and shared micro-mobility fleets. We introduce a mixed-integer programming model that incorporates preferences into the objective function and solve it for real transport network data in a suburban area of Rotterdam. Model application results demonstrate that the proposed algorithm can efficiently obtain near-optimal solutions. Managerial insights are gained from comprehensive experiments that consider various passenger segments, costs of micro-mobility vehicles, and availability fluctuation of shared mobility. open-access, with Yimeng Zhang and Shadi Sharif Azadeh, part of the SUM Project funded by the European Commission, in collaboration with RET.

📘 Artificial Intelligence in Urban Planning and Design: Technologies, Implementation, and Impacts As cities become more data-driven and complex, AI is no longer a futuristic concept in urban planning — it is an active design force. 🌆🤖 This comprehensive resource explores how Artificial Intelligence is transforming smart city planning and urban design. 🔍 Why This Matters It goes beyond surface-level discussion and provides: → 🧠 A clear foundation of AI theory in the context of urban systems → 🏙️ Real-world applications of AI in city planning and design → 📊 AI-driven research and information systems → 🎨 Generative design frameworks powered by AI Rather than presenting AI as a single tool, it positions AI as a structural shift in how cities are analyzed, modeled, and designed. 🚀 A New Design Paradigm One of the most compelling themes is the rise of AI-generated planning solutions — often created without predefined rules. This introduces powerful opportunities: ✔️ Adaptive urban modeling ✔️ Data-informed infrastructure planning ✔️ Dynamic simulation of growth scenarios But it also raises critical questions: • Who defines the objectives? • How do we ensure transparency? • What happens to traditional planning expertise? 🧩 Theory Meets Practice It bridges: 🔹 Theoretical foundations of AI 🔹 Practical implementation in urban systems 🔹 Critical evaluation of tools and methodologies 🔹 Future directions for responsible AI integration AI is not treated as a silver bullet. Instead, both potential and limitations are examined with balance. 🌍 The Bigger Picture Urban environments are living systems — socially, economically, and environmentally interconnected. AI introduces the possibility of: • More resilient city planning • Optimized resource allocation • Smarter infrastructure design • Human-centered urban innovation Meaningful progress requires thoughtful governance and intentional design. AI in urban planning isn’t just about smarter cities. It’s about designing cities that remain human at scale. Follow and Connect: Woongsik Dr. Su, MBA #ArtificialIntelligence #UrbanPlanning #SmartCities #GenerativeDesign #DigitalTransformation #UrbanInnovation #CityPlanning #AIInDesign

The strategy "Better Connected: A Strategy for Integrated Transport" outlines a "high-velocity" transformation of the UK's transport digital estate, aiming to create a "seamlessly integrated" multi-modal network that prioritizes passenger experience and operational resilience. By leveraging Agentic AI and "intelligent logistics," the Department for Transport seeks to bridge the "foundational gap" between fragmented local services, moving toward a system of "proactive resilience" where real-time data orchestration slashes delays and boosts reliability. This "Science for Policy" approach focuses on "recursive synthesis" of data across bus, rail, and automated passenger services (APS) to ensure the network is "healthy, equitable, and resilient," ultimately preventing a "lost decade" of transport stagnation and supporting the nation's long-term economic and environmental goals. ➡️ Social Factors Passenger-Centric Mobility: A primary social objective is to improve "urban livability" by ensuring that transport systems are inclusive and easy to navigate for all citizens, reducing the social friction associated with fragmented travel. Shift in Commuter Behavior: The strategy reflects a social move toward "flexible ownership" and shared mobility, where the focus shifts from vehicle possession to "mobility as a service" (MaaS), requiring a high-end requirement for user-friendly digital interfaces. ➡️ Technological Factors The Rise of Automated Passenger Services (APS): Technology acts as an "essential enabler" through the development of permitting schemes for automated services, representing a "breakout moment" for autonomous transit in the UK. Digital Twins and Real-Time Orchestration: The strategy utilizes high-end compute capability to create digital projects that "slash delays" on regional roads by using AI for real-time traffic and bus reliability management. ➡️ Economic Factors Productivity and Reliability Gains: From an economic perspective, "slashing delays" is a "cross-cutting lever" for national productivity, as more reliable transport estates reduce the wasted time and costs associated with congestion. ➡️ Environmental Factors Decarbonization through Integration: A core environmental goal is to reduce the carbon footprint of the transport sector by making public and shared transit more attractive than private car use, thus supporting "nature-positive" urban development. Optimization for Efficiency: By using "cutting-edge technology" to boost bus reliability and road efficiency, the strategy aims to minimize idling and wasted energy, aligning transport innovation with national climate resilience targets. ➡️ Political & Regulatory Factors Strategic Governance and "Science for Policy": The report represents a "Science for Policy" framework, where evidence-based innovation and technology plans are used to draft the "regulatory fabric" for future transport systems.

I turned Singapore's road network into a graph. Here is what it revealed for urban decision-making. Using OSMnx, NetworkX, and Python, I built a morphological graph of Singapore's street network. The output is a topological map of approximately 15,000 intersections connected by roughly 30,000 road segments spanning thousands of kilometers. This is not just a visualization. It is a decision-making tool. Six practical insights this graph enables: One, identify critical intersections. Nodes with high degree connect six or more roads. If one of these fails, a large area becomes paralyzed. These intersections deserve priority maintenance and redundant traffic systems. Two, optimize emergency response. Calculate five-minute coverage zones from fire stations and hospitals. Find underserved neighborhoods before an incident happens, not after. Three, guide retail placement. A convenience store at a high-degree intersection reaches three times more passing traffic than one on a quiet street. Delivery hubs and billboards belong at these nodes. Four, detect accessibility gaps. Connected components reveal isolated communities. If a neighborhood sits in a small component, residents have fewer route options and longer emergency travel times. Build bridges or add alternative routes. Five, predict congestion bottlenecks. Betweenness centrality identifies roads that carry the most through traffic. Adjust signal timing and lane allocation before congestion becomes chronic. Six, support environmental planning. Low network density and long road segments correlate with urban heat. These become priority zones for tree planting and shade infrastructure. The graph is now ready for Graph Neural Networks. Adding bus stops, planning zones, and contiguity edges will transform it into a heterogeneous urban graph for predictive modeling. All built with open tools and in Python. #UrbanAnalytics #GraphTheory #NetworkX #Singapore #SmartCity #TransportPlanning #DataScience #Geospatial

The 'Just-in-Time' Transport Paradox: Balancing Lean with Global Volatility The Problem: Just-in-Time (JIT) promises reduced inventory and responsiveness. However, recent global disruptions (pandemics, geopolitical shifts) exposed its fragility. Lean supply chains, optimized for stability, struggle with volatility, leading to stockouts and production halts. The challenge: harness JIT benefits in transport while building robust resilience against an unpredictable global environment. The Expert Insight: The JIT Transport Paradox demands evolving JIT from dogma to a flexible, adaptive strategy. This means integrating 'Just-in-Case' resilience: intelligent inventory positioning, diversified multi-sourcing, dynamic routing, and real-time visibility. The goal is 'Just-in-Case-of-Disruption' agility – a balance that preserves JIT efficiency while embedding robustness to absorb and recover from shocks. This ensures continuous operational flow without reverting to wasteful, excessive inventory. My experience in strategic planning, risk management, and Lean implementation is crucial for this balance. Actionable Steps for Balancing JIT with Resilience in Transport: 1. Segment Supply Chain & Differentiated JIT: Apply strict JIT for stable, low-risk items. For high-risk, high-value, or volatile components, strategically build intelligent buffers based on criticality and lead time reliability. 2. Implement Robust Multi-Sourcing & Nearshoring: Diversify your supplier base and explore nearshoring/reshoring for critical components to shorten lead times and reduce transit risks. 3. Leverage Advanced Demand Sensing & Predictive Analytics: Use AI/ML to improve forecasting accuracy and proactively predict disruptions (supplier failures, port congestion, weather). This enables dynamic adjustments to transport schedules and inventory. 4. Build Dynamic Routing & Flexible Capacity: Implement advanced Transport Management Systems (TMS) with dynamic routing that adapts in real-time. Develop flexible carrier contracts for rapid scaling of transport capacity in response to demand or disruptions. 5. Establish Strategic Inventory Buffers: Position buffer stock for critical components or finished goods at regional distribution centers. These act as shock absorbers, preventing minor disruptions from cascading into widespread failures. Conclusion: The JIT Transport Paradox highlights the need for adaptive, intelligent, and resilient Lean logistics. By thoughtfully integrating 'Just-in-Case' mechanisms, businesses maintain JIT efficiency while building a supply chain robust enough to thrive in an unpredictable world. Is your JIT strategy a source of unwavering strength, or does it harbor hidden fragilities? #JIT #LeanLogistics #SupplyChainResilience #RiskManagement #TransportManagement #GlobalSupplyChain #Volatility #StrategicPlanning #InventoryManagement #DigitalTransformation

🚇 [JUST PUBLISHED!] How can flexible train formation and skip-stop operations enhance the efficiency of urban rail transit? This new study by @Feng Li, @Yue Zhang, @Xin Guo, and @Tingxu Chen introduces an integrated optimisation framework that synergises flexible train formation and skip-stop strategies to boost operational efficiency and capacity utilisation. Key takeaways: 🔍 The proposed model optimises train stop schedules, arrival and departure timings, and formation configurations to improve urban rail system performance. 🚉 Flexible train formation dynamically adjusts capacity through coupling/decoupling, aligning real-time service with fluctuating passenger demand. ⏩ Skip-stop strategy reduces travel time by selectively bypassing stations while maintaining accessibility and safety constraints. 📊 Case study on Beijing Subway Line 9 demonstrates: ✅ 24.8% fewer stranded passengers ✅ 13.2% reduction in average waiting time ✅ 14.1% fewer train formations used 🧠 The study’s dual-strategy coordination mechanism establishes a data-driven foundation for intelligent rail transit scheduling and congestion mitigation. 🔗 Read the paper: https://bit.ly/48yKTaT #UrbanRailTransit #TimetableOptimisation #FlexibleTrainFormation #SkipStopStrategy #TransportPlanning #SmartMobility #TransitEfficiency #SustainableTransport #publictransport #scheduling #timetabling

𝗦𝗺𝗮𝗿𝘁𝗲𝗿 𝗥𝗼𝗮𝗱𝘀 𝗔𝗿𝗲 𝗤𝘂𝗶𝗲𝘁𝗹𝘆 𝗜𝗺𝗽𝗿𝗼𝘃𝗶𝗻𝗴 𝗙𝗿𝗲𝗶𝗴𝗵𝘁 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 Over the last few years, I've noticed an important shift across logistics corridors. Infrastructure is no longer only about building roads. It is increasingly about making roads 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁. With the expansion of access-controlled highways, adaptive signal systems, and real-time traffic visibility platforms, smart corridors are beginning to influence how efficiently trucks move across networks. 🚛 𝗪𝗵𝘆 𝗦𝗺𝗮𝗿𝘁 𝗥𝗼𝗮𝗱𝘀 𝗠𝗮𝘁𝘁𝗲𝗿 𝗳𝗼𝗿 𝗙𝘂𝗲𝗹 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 Road freight still carries nearly 60% of India's cargo movement — even small improvements in driving conditions create meaningful cost and emission benefits. Studies across Intelligent Transport System (ITS) corridors show: 📡 Real-time traffic systems → fuel efficiency up 5–10% 🛣️ Access-controlled highways → fuel performance up 10–20% 🚦 Adaptive signal coordination → idling reduced by 15–30% 🧭 Smart routing tools → trip fuel use down 8–15% These are not marginal gains. They directly improve fleet productivity and operating economics. 📊 𝗪𝗵𝗮𝘁 𝗜𝘀 𝗖𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗼𝗻 𝘁𝗵𝗲 𝗚𝗿𝗼𝘂𝗻𝗱 Smart roads support freight movement through: 👁️ Better traffic visibility — drivers avoid congestion and maintain smoother speeds 🛣️ Stable cruising environments — access-controlled highways reduce interruptions 🚦 Reduced intersection delays — adaptive signals lower idle time and fuel burn 🧭 Digital route planning — dynamic routing helps fleets choose efficient corridors 🌱 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝗦𝘂𝗽𝗽𝗹𝘆 𝗖𝗵𝗮𝗶𝗻 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 Road freight contributes roughly 7–8% of global CO₂ emissions. Corridor-level efficiency improvements are one of the fastest ways to reduce logistics emissions. From my perspective, smart infrastructure is no longer only a transport upgrade. It is becoming a 𝘀𝘂𝗽𝗽𝗹𝘆 𝗰𝗵𝗮𝗶𝗻 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗶𝗲𝗿 — improving fuel efficiency, delivery predictability, and sustainability at the same time. Because when roads become smarter, freight networks become stronger. 🚛📊🌱 #SmartLogistics #FreightEfficiency #SupplyChain #IntelligentTransport #smart #green #truck #trucking #logistics #transport #transportation #GreenFreight #IndiaLogistics #hsbanga Harvinder Singh Banga

Seven months ago, I stood in front of a defense panel and defended my final-year project that answered a simple question: "Can we use Reinforcement Learning (RL) to build an intelligent, adaptive traffic control system that outperforms traditional fixed-time signals in managing the complex, mixed-traffic patterns of African cities?" I've talked about this project offline at global conferences with colleagues and mentors who pushed back on my assumptions. Time to tell it here too 👇 🚥 The backstory: On a Sunday evening in Kigali, racing to beat a deadline, my bike stopped at an empty 4-way intersection. No vehicle, no reason to wait, except the signal timer had just reset to 60 seconds on red. Every minute we spent waiting mattered, and I thought about ambulances stuck at similar signals and how survival rates for out-of-hospital cardiac arrest drop 7-10% for every minute of delay, with almost no chance of survival past 8 minutes That experience became my summative project, where I evaluated 4 RL algorithms across hyperparameter configurations on simulated Rwandan junctions. PPO won clearly, consistently learning better signal-timing policies For my thesis the following term, I took the project further: 300+ papers reviewed, 71 directly relevant & 15 regional sources 💡What I found: → Lagos, my hometown, was ranked the world's most congested city in 2025, with 70 minutes per one-way commute and $4.8 billion annual loss → Africa's population is expected to reach 2.5 billion by 2050, with 64% living in cities. This explosive growth places much pressure on transportation networks that are often inadequate to meet the surging demand → Adaptive traffic systems that could help already exist; they're just unaligned with African realities and overpriced 🤔 Then I narrowed the question: "Could RL make adaptive traffic control affordable enough for cities like Lagos, Nairobi, or Kigali to actually deploy?" I trained the best PPO variant further with domain randomization, multi-seed training & safety wrapper. Then, took it off simulation and onto a Raspberry Pi to validate it under real hardware constraints, a step most RL traffic research skips It worked 🤭 ✅ 8.9% vehicle delay reduction ✅ 8.8% queue reduction ✅ 72% win rate vs fixed-timing (p<0.05) ✅ 6.84ms mean inference latency (14.6× safety margin) with best-case as 5.76ms (17× margin) ✅ Zero crashes, 100% throughput ✅ Reproducibility (CV=1.3%) The technical work proved the idea is affordable. I also developed a "Mobility Justice" framework, ensuring affordable doesn't come at the cost of fair ⁉️ Now, the question is no longer "can intelligent traffic management work in Africa?" but "which city will be first to deploy?" I'm exploring what deployment could look like. If you're working on AI ethics, transportation infrastructure, or public-interest technology in African cities, let’s connect 🌍 #machinelearning #trafficcontrol #reinforcementlearning #projectdefense