Transportation Management System Features

Securing the Transport Sector !!! The EU state of cybersecurity report shows that Transport Sector is the second most targeted sector (at 11 percent) during the previous year. It includes rail, aviation, maritime, and road systems. Which are increasingly interconnected, making it a prime target for cyber threats. With operational technology (OT) merging with IT, vulnerabilities in legacy systems and emerging technologies pose risks to safety, continuity, and national security. Top Risks in Transport Cybersecurity: 1. Critical System Breaches: Attacks on signaling systems, GPS, or automated controls can cause disruptions or accidents. 2. Ransomware: Threat actors target passenger systems and logistics operations for maximum impact. 3. Third-Party Vulnerabilities: Supply chain dependencies and contractors introduce new risks. What can we do to ensure resilience: ✏️ Layered Defense: Implement robust defense-in-depth strategies to secure endpoints, networks, and critical systems. ✏️ Standards Adherence: Ensure compliance with frameworks like NIST Cybersecurity Framework, IEC 62443, and ISO 27001 for OT environments. ✏️ Threat Intelligence: Leverage sector-specific intelligence to preemptively address emerging threats. ✏️ Incident Preparedness: Regularly test incident response and recovery plans under simulated attack conditions. Key areas to focus: ✏️ Segmented Networks: Isolate operational networks to limit exposure. ✏️ Real-Time Monitoring: Deploy solutions for anomaly detection and rapid containment. ✏️ Supply Chain Security: Strengthen vetting processes for vendors and contractors. To ensure resilience, we need to go beyond protection—it’s about enabling trust in the systems that move people and goods worldwide. Proactive measures today ensure secure, uninterrupted journeys tomorrow. What are your strategies for tackling transport sector cybersecurity challenges? #TransportSecurity #CyberResilience #CriticalInfrastructure #OTSecurity

Exciting research area we’re exploring at Google: simulations on urban mobility. Simulations can help city planners anticipate congestion (and pollution!) before it happens. What's a mobility simulation? Virtual replicas of cities that can simulate and predict traffic patterns, identify bottlenecks, and test potential solutions - all before impacting real-world commuters. Significant Travel Time Reduction Our model achieves up to a 78% reduction in path travel time nRMSE during peak hours (Boston, 7pm) and 76% during non-peak hours (Orlando, 3pm) How Does It Work? We combine our understanding of the road network and with historical Google Maps driving trends to create accurate models (especially of mobility demand, which is one of the key parameters). Our metamodel calibrates traffic simulations 52% better than the baseline method. Our team constructed simulations for six metropolitan areas: - Seattle - Denver - Philadelphia - Boston - Orlando - Salt Lake City. Learn more about this early research: https://lnkd.in/gyQzXqUg Congratulations Carolina Osorio, Chao Zhang, Neha Arora and team!

After thinking deeply and practically as an operations research practitioner working on transforming Toyota North America’s supply chains and logistics, here’s how I’ve come to think about yard scheduling and sequencing in real-world operations. In yard logistics, deciding how to handle scheduling and sequencing often comes down to a fundamental design choice: do you build one end-to-end optimization model, or break the problem into a hierarchical structure? Both approaches have merit, but the right choice depends on the maturity of your operation, your data quality, and your need for responsiveness. An end-to-end model treats the entire yard as a single optimization problem. From the moment a vehicle arrives at the gate to the point it exits after fueling, staging, and processing, every step is scheduled together. The advantage is clear: this global view can yield truly optimized flows, minimize bottlenecks, and align all decisions toward throughput and efficiency. However, this approach is heavily reliant on high-fidelity data across all zones of the yard. If there are unexpected delays (like a worker calling in sick, a fueling station going offline, or a shuttle arriving late) the whole plan can become fragile. In practice, this kind of system also tends to be computationally heavier and slower to react in real time unless reoptimization is thoughtfully designed. A hierarchical model breaks the yard down into zones or functions. High-level scheduling might determine when a shuttle should move workers, or when a batch of cars should be released to fueling. Then, each zone locally decides how to sequence its tasks. This makes the system more resilient, local disruptions can be handled without having to re-optimize everything. It’s often easier to implement and more forgiving of messy, real-world variability. But the downside is that decisions made independently in one zone may not be optimal when viewed from the yard’s perspective as a whole. You may reduce wait time in one area only to create backups downstream. In my experience working in yard scheduling, the best path forward is staged. If your data systems are still maturing, start with a hierarchical model. It’s practical, easier to maintain, and more robust to the day-to-day volatility of yard operations. As your visibility improves and your systems become more integrated, evolve toward an end-to-end approach, or better yet, implement a hybrid system. In this setup, global optimization runs periodically throughout the day, reoptimizing as new information comes in (whether it’s a backlog at a station, a no-show worker, or a delay in vehicle arrivals). Meanwhile, local zones retain autonomy to adjust sequencing based on real-time conditions. This balance between strategic coordination and operational flexibility is where the real performance gains are unlocked. #YardScheduling #Optimization #SupplyChainExecution #OperationsResearch #DecisionIntelligence #RealTimeOptimization #Logistics

What is an advanced driver assistance system (ADAS)❓️ How does it work🤔 Advanced Driver Assistance Systems (ADAS) aim to improve vehicle safety and driving comfort by automating, adapting, or enhancing various driving functions. ➡️Using a combination of sensors, cameras, radars, and other advanced technologies, ADAS gathers data from the vehicle's environment to support the driver through alerts or by taking control in specific situations. Automatically adjusts the vehicle’s speed to maintain a safe distance from the vehicle ahead. 1️⃣ Key Components of ADAS 🎦 Sensors and Cameras: 🔹️Radar: Detects the distance and speed of objects. 🔹️Lidar: Measures distance by illuminating the target with laser light. 🔹️Cameras: Provide visual data and are used for object detection, lane detection, traffic sign recognition, etc. 🔹️Ultrasonic Sensors: Used for close-range object detection, such as in parking assist. 2️⃣ Data Processing and Control Units: The vehicle’s Electronic Control Unit (ECU) processes data from the sensors and cameras to interpret the environment and make decisions. ✅AI and Machine Learning: Some ADAS features rely on AI algorithms to improve decision-making by learning from driving patterns and environmental conditions. ✅CAN (Controller Area Network) Communication: ADAS components communicate over the CAN bus, transmitting sensor data and control signals between various systems in the vehicle. 3️⃣ Common ADAS Features ✅Adaptive Cruise Control (ACC): Automatically adjusts the vehicle’s speed to maintain a safe distance from the vehicle ahead. ✅Lane-Keeping Assist (LKA): Monitors lane markings and helps steer the vehicle to prevent unintentional lane departure. ✅Blind Spot Detection: Alerts the driver if there’s a vehicle in their blind spot when changing lanes. ✅Automatic Emergency Braking (AEB): Automatically applies brakes to prevent or mitigate a collision. ✅Traffic Sign Recognition: Uses cameras to identify and display traffic signs to the driver. ✅Parking Assist: Helps the driver park the vehicle using sensors to detect obstacles and steer the vehicle into place. 4️⃣ Levels of Automation ADAS can be classified into five levels of automation, from Level 0 (no automation) to Level 5 (full autonomy): 5️⃣ Benefits of ADAS 🚀 Enhanced Safety: Helps reduce accidents caused by human error (e.g., fatigue, distraction). 🚀 Improved Traffic Efficiency: Features like adaptive cruise control can lead to smoother traffic flow. 🚀 Reduced Stress: Parking assist and other features can make driving easier and more comfortable.

🚀 Walmart’s E-Commerce Surge: $121B in 2024, Profitability Achieved! 💰 While Amazon often dominates e-commerce headlines, Walmart has been quietly revolutionizing its online presence: • $120.9B in online sales in 2024, marking a 21% year-over-year increase. • E-commerce now constitutes 18% of total revenue, up from 13.6% in 2023. • A remarkable 47% growth in just two years. The catalyst? A strategic overhaul of their supply chain and delivery systems: • Transitioned from traditional ZIP code mapping to a honeycomb-style hexagonal system, enhancing delivery efficiency. • Expanded same-day delivery reach to 93% of U.S. households, with plans to achieve 95% coverage by end of 2025. • The Spark delivery platform, leveraging geospatial technology, added 12 million new households to its network in January alone. These innovations have led to significant operational efficiencies: • 30% of U.S. orders now utilize fast delivery options. • Delivery cost per order decreased by 20% in Q4. • Walmart’s U.S. e-commerce sector achieved profitability for the first time in Q1 2025. David Guggina, Executive VP and Chief eCommerce Officer, emphasized the “flywheel effect”: “When customers choose fast delivery, they shop more frequently, buy a broader range of items, and basket size increases.” This transformation underscores a pivotal lesson for retailers: Seamless integration between physical stores and e-commerce platforms is not just beneficial—it’s essential. Walmart’s journey from a traditional brick-and-mortar giant to a formidable e-commerce contender exemplifies the power of strategic innovation and adaptability. #Ecommerce #RetailInnovation #DigitalTransformation #Walmart #SupplyChain #Logistics #Omnichannel #RetailStrategy

Advanced Driver Assistance Systems (ADAS) are designed to enhance vehicle safety and improve driving comfort by automating, adapting, or enhancing certain driving tasks. ADAS relies on a combination of sensors, cameras, radars, and other advanced technologies to collect data from the vehicle's surroundings and then assist the driver by providing alerts or taking control in certain situations. These systems can range from simple features like parking sensors to more complex systems like autonomous emergency braking (AEB) or lane-keeping assist (LKA). ⭕ Key Components of ADAS ✅ Sensors and Cameras: Radar: Detects the distance and speed of objects. Lidar: Measures distance by illuminating the target with laser light. Cameras: Provide visual data and are used for object detection, lane detection, traffic sign recognition, etc. Ultrasonic Sensors: Used for close-range object detection, such as in parking assist. ⭕Data Processing and Control Units: The vehicle’s Electronic Control Unit (ECU) processes data from the sensors and cameras to interpret the environment and make decisions. ✅AI and Machine Learning: Some ADAS features rely on AI algorithms to improve decision-making by learning from driving patterns and environmental conditions. ✅CAN (Controller Area Network) Communication: ADAS components communicate over the CAN bus, transmitting sensor data and control signals between various systems in the vehicle. ⭕Common ADAS Features ✅Adaptive Cruise Control (ACC): Automatically adjusts the vehicle’s speed to maintain a safe distance from the vehicle ahead. ✅Lane-Keeping Assist (LKA): Monitors lane markings and helps steer the vehicle to prevent unintentional lane departure. ✅Blind Spot Detection: Alerts the driver if there’s a vehicle in their blind spot when changing lanes. ✅Automatic Emergency Braking (AEB): Automatically applies brakes to prevent or mitigate a collision. ✅Traffic Sign Recognition: Uses cameras to identify and display traffic signs to the driver. ✅Parking Assist: Helps the driver park the vehicle using sensors to detect obstacles and steer the vehicle into place. ⭕Levels of Automation ADAS can be classified into five levels of automation, from Level 0 (no automation) to Level 5 (full autonomy): ⭕Benefits of ADAS ✅Enhanced Safety: Helps reduce accidents caused by human error (e.g., fatigue, distraction). ✅Improved Traffic Efficiency: Features like adaptive cruise control can lead to smoother traffic flow. ✅Reduced Stress: Parking assist and other features can make driving easier and more comfortable. ADAS technologies are rapidly advancing, driving the future of autonomous vehicles while improving everyday driving safety and comfort.

Kinematic Equations in ADAS Advanced Driver Assistance Systems (ADAS) rely on kinematic equations to predict motion, avoid collisions, and keep drivers safe. These equations help calculate: ✔ How fast a car is moving (velocity) ✔ How quickly it can stop (deceleration) ✔ How far it travels before reacting (stopping distance) v=v₀+at: Final velocity. d=v₀t+½at²: Displacement. v²=v₀²+2ad: Velocity-displacement relationship. d=1/2(v₀+v)t: Displacement with average velocity. Where: v = final velocity v₀ = initial velocity a = acceleration t = time d = displacement Automatic Emergency Braking (AEB): Scenario: Your car is traveling at 20 m/s (v₀). A pedestrian suddenly appears 25 meters ahead (d). The AEB system needs to determine the deceleration (a) required to stop in time. Calculation: We know the final velocity (v) must be 0 m/s to stop. Using v²=v₀²+2ad, we can rearrange to solve for a: a=(v2−vo^2)/(2d) a=(0^2−20^2)/(2∗25) −400/50 =-8m/s² This means the AEB system needs to apply a deceleration of 8 m/s² to avoid a collision. Adaptive Cruise Control (ACC): Scenario: Your car is traveling at 30 m/s (v₀). The car ahead slows down, causing your radar to detect a relative acceleration of -2 m/s² (a). The system wants to calculate the distance (d) your car will travel in the next 3 seconds (t). Calculation: Using the equation d=Vot+1/2at^2 d=(30∗3)+(0.5∗−2∗3^2) d=90+(−9) d=81 meters. The system calculates your car will travel 81 meters in that time frame. This information is used to adjust your car's speed to maintain a safe following distance. Time-to-Collision (TTC): Scenario: Your car is traveling at 15 m/s, and a car ahead is traveling at 10 m/s. The distance between them is 25 meters. Assuming the car ahead maintains its velocity. How much time until a collision? Calculation: Relative velocity = 15-10 = 5 m/s. TTC = distance / relative velocity. TTC = 25 / 5 = 5 seconds. Therefore, the time to collision is 5 seconds. If the car ahead were decelerating, then the "a" variable would be used to calculate a more accurate TTC. Key ADAS Points Sensors (radar, lidar, cameras) provide the initial data (position, velocity). ADAS algorithms use kinematic equations to: Predict future object positions. Calculate safe following distances. Determine necessary braking or steering actions. The accuracy of these calculations is critical for the reliable operation of ADAS. Disclaimer: This post is intended for educational purposes from a software development perspective. It does not cover complex non-linear vehicle dynamics.

See this post "Inside Glovo’s deliveries optimization" from Glovo. Efficiency is the key to success in the instant-delivery business. The Matching Team from Glovo has developed innovative methods to optimize order assignments for couriers. Here’s how they do it: → Assignment Problem: Glovo tackles the challenge of assigning the best courier for each order by using algorithms like the Hungarian Algorithm and Minimum Cost Flow. They aim to reduce courier waiting times and improve delivery speed without violating business constraints. → Bundling Strategy: Combining multiple orders into a single delivery, or bundling, allows them to increase efficiency, but it’s not simple! They consider real-time factors like preparation time, travel distance, and traffic conditions. → Multibundling & Scaling: They extended bundling to multiple orders across different locations, but handling such complexity required solving the Vehicle Routing Problem (VRP) efficiently, especially when considering millions of orders and couriers. → Architecture: To manage ~20 million assignments monthly (that's back in 2023, they rely on an advanced system architecture using relational databases, Redis for geo-indexing, and scalable instances. All decisions are processed in under 10 seconds per city. → Continuous Optimization: Real-time data and predictions help them adapt to changing circumstances, ensuring we always make the most efficient choices. With technology and algorithms, they're pushing the boundaries of what’s possible in delivery optimization. 🚴♂️📦

In the context of electrifying paratransit, we investigate heuristic scheduling of minibus taxis in South Africa's eventual electrified paratransit. Link: https://lnkd.in/dCYvDaMd The proposed method supports: - multiple ranks/depots, - mixed fleet (electric and ICE) deployment, - number of charging point optimisation, - partial and opportunistic charging of vehicles, and - scheduling with load shedding (or to avoid peak tariff times) Abstract: The predominant mode of public transport in South Africa originates from the informal sector, specifically “paratransit”. Vehicles carry up to 23 passengers and are still propelled by internal combustion engines. We investigate the feasibility of using electric vehicles without negating the loss of opportunities by drivers and owners. We propose that scheduling of the electric vehicles is one important cornerstone towards electrification. We developed a fast-executing heuristic scheduling algorithm that allows for multiple vehicle depots in the transport network; simultaneous electric and internal combustion engine vehicle deployment; determining the number of charging stations; partial charging; and scheduled charging with intermittent electricity supply. The scheduling algorithm achieves the minimum number of vehicles to execute the passenger demand in shorter total distances, outperforming current approaches. The algorithm demonstrated multi-objective optimisation by minimising the vehicles, the number of charging stations, and the average trip delays of a schedule. With Jacques Wust and James Bekker. Industrial Engineering | Stellenbosch University Electrical & Electronic Engineering Department (Stellenbosch University) Related paper: Scheduling with mixed fleets to improve the feasibility of electric minibus taxis: a case scenario of South Africa: https://lnkd.in/deBrfug8 For more: https://lnkd.in/dukgNDkj

🚦Data Sharing Policy of Indian Ministry: The NTR Data Sharing Framework The Ministry of Road Transport & Highways has just unveiled its Policy for Data Sharing from the National Transport Repository (NTR). For the uninitiated, NTR isn’t just a databaseit’s a living digital twin of India’s roads and drivers, housing records from Vahan, Sarathi, e-Challan, eDAR, and FASTag. That’s 39 crore vehicles, 22 crore licences, and crores of transactions all in one place. So what’s new? Legal backbone: For the first time, the DPDPA 2023 is woven directly into how transport data can be accessed, making MoRTH the primary Data Fiduciary. Data recipients from #police to #insurance companies to fintechs are now legally on the hook for breaches. - Granular guardrails: APIs, login-based portals, password-protected bulk transfers, and mobile apps will all coexist but with strict consent + security audits (CERT-IN certification is mandatory). - Citizens get power too: You can now pull your own data securely via mParivahan/DigiLocker, and even limited verification of others’ RC/DL is possible with safeguards. - Academia & innovation boost: Aggregated, anonymised datasets will be opened up under the NDSAP 2012, fuelling research and smart mobility solutions. But here’s the real inflection point: India is moving from data as government property ➝ to data as a fiduciary duty. This subtle shift aligns us with global norms while preserving sovereignty. Yet, the implementation risks are stark misuse by private players, consent fatigue for citizens, and the perennial challenge of securing state-level enforcement. My View - - This policy is not just about sharing transport data. It’s a test run for India’s data federalism balancing central control, state co-ownership, and citizen rights. If executed right, it could become the model for other national repositories (health, education, finance). If not, we risk turning highways of data into highways of surveillance. Policy is Attached here below - What do you think: Will this framework protect citizen trust while enabling #innovation, or will it become another compliance maze for stakeholders? #CyberLaw #DPDPA #DataGovernance #TransportPolicy #Privacy #DigitalIndia #technology #aadhaar #kyc #gdpr #policy #datasharing #data