Wie kann Operations Research den Verkehrsfluss in Städten verbessern?

Several methodologies: • Traffic Mathematically Modeling • Finding Optimal Routing • Network Optimization Problems • Dynamic Traffic Management by Dynamic Networking • Public Transportation Modelling and Scheduling • Forecasting • Sustainable Models for Traffic Policies • Parking Management

When people say "Operations Research" (OR), they usually mean 1 of the following: 1. Mathematical Optimization. Think of mathematical programming like linear programming (LP) mixed integer programming (MIP). etc. 2. The following: ✅ Mathematical Optimization ✅ Simulation ✅ Queuing Theory ✅ Forecasting ✅ Probability ✅ Statistics ✅ Machine Learning In other words, OR can be interchangeable with data science (DS), depending on who you are talking with.

OR can help optimise city traffic in several ways. Few could be:- 1. Dynamic signal time, currently most of traffic signals are either fixed time interval signal or manual signal both are inefficient inherently OR can help study traffic volume flow from all directions(using data from cameras) and schedule an optimal signal time, change frequency and pass /hault time length. 2. OR can help solve lane problem. Firstly unidirectional lanes considering different kind of vehicles(by dimensions, prescribed speed limits), also bidirectional lane width specially on roads with no fixed dividers as usually bidirectional flow volume is not similar one side have higher volume than other. 3. Alternate route & time based on congestion pattern.

Operations Research (OR) can significantly improve urban traffic flow through optimization and simulation. It can fine-tune traffic signal timings based on real-time conditions to minimize wait times. OR can also optimize public transport routes to reduce congestion and improve reliability. Additionally, traffic routing algorithms can offer real-time, optimal driving routes to distribute traffic evenly. These OR methods collectively contribute to more efficient and less congested urban transportation systems.

Operations Research can improve traffic flow in cities by optimizing traffic signal timings in real-time, developing efficient route planning algorithms, enhancing public transportation systems, simulating traffic flow scenarios, and implementing dynamic traffic control systems. These OR techniques help reduce congestion, improve traffic management, and enhance transportation efficiency within urban areas

You can create simulation models simulate traffic. What's nice about simulation modeling is that it's very cheap and you try a variety of things quickly. Let's see how traffic flows if we have a traffic light at the intersection. What if we make it a 4 way stop instead? How would traffic flow if it was a roundabout instead? It would be very time consuming and expensive to try all of the previously mentioned scenarios in reality. However, it's quick and cheap to simulate each scenario.

What about a game-theoretical perspective? Game Theory studies interactions between individuals/agents. Using its amazing modeling approach, Game Theory can help improving traffic flow for the best of public welfare. A one-stage, simultaneous, n-player game would suggests routes for individuals and provide insights, even though practical implementation might be a challenge.

Operations Research (OR) is instrumental in optimizing transportation, particularly in traffic management. It merges transport science and OR to enhance efficiency in several key areas: 1. Traffic signal synchronization through predictive modeling and mixed-integer programming to alleviate congestion. 2. Efficiency upgrades at roundabouts and intersections using discrete event simulation backed by advanced tools. 3. Toll pricing strategy development to distribute traffic across routes for better load management. 4. Public transit load balancing and schedule optimization, ensuring buses and trains run at ideal capacities and intervals. Such applications showcase OR's critical role in advancing and managing traffic flow effectively.

- Optimizes traffic signal timing for reduced congestion and shorter waiting times. - Algorithms for route planning and traffic prediction, guiding drivers to less congested routes. - Congestion pricing schemes, designed with OR, encourage off-peak travel and alternative transportation options. - Traffic flow simulation models help evaluate proposed changes and dynamic traffic management systems respond in real-time. - Multi-modal transportation integration is facilitated, promoting various transportation modes. - Big data and machine learning are used to analyze real-time traffic data, enabling dynamic traffic optimization.

It's a traffic planning problem! The main problem will come when we make the actual requirement document. Formulation will come second, but the constraints, traffic rules or government imposed rules gathering will be a real challenge. Simulation might be a good approach as OR formulation would be too complex to run in manageable time. One can go for a reinforcement learning approach along with a game theory strategy that might be useful. But again, making the requirement document for optimizing traffic flow would be a real challenge.

A few applications that come to mind in general: 1. Route optimization to reduce congestion. (Car pooling, ride share, pedestrian and bike routing, public transportation routing, dynamic traffic assistant for real time routing) 2. Traffic signal timing to improve flow. 3. Predictive modeling for traffic forecasting. 4. Traffic incident management strategies. 5. Toll pricing and demand management. 6. Evaluation of infrastructure investments.

OR can employ machine learning to model current traffic patterns. This model that can be used to simulate the effect of introducing new traffic flow alternatives/variables (lights, bridges, mass transit, exits, etc.) to maximize traffic flow at the lowest cost, risk, and innumerable other variables.

Some interesting applications of OR are: - Dynamic Re-Routing and FUA (Flexible Use of Airspace) with heuristic and metaheuristic algorithms: Heuristic and metaheuristic approaches, such as genetic algorithms, are applied to find near-optimal solutions for dynamic re-routing of flights. These techniques help adapt the use of airspace in real-time to optimize traffic flow. - Slot Allocation and Scheduling with Integer Programming Models: OR models, particularly integer programming, are used for slot allocation and scheduling. These models help optimize the assignment of arrival and departure slots to flights, considering various constraints such as airport capacity, airspace constraints, and airline preferences.

Too many comments assume that the complex of traffic flow problems can be solved by the classic OR in textbooks. THEY CAN'T! What are the objectives? should all vehicles be able to proceed as fast as possible? Is car use to be discouraged? Are walking, cycling and shared transport trips preferred? Each will result in its own configuration of the same set of streets. With ICT, analysis of population structures, land use, behavioral tools like Mosaic etc. we should be abler to build far more efficient trans[port networks, even without new infrastructure,than we currently have. Many start ups,researchers and ICT suppliers have developed methods, tools and apps solving sub-problems. Now to share all stakeholders' data and optimise!

Some key benefits are: - Reduce delays and congestion: improving overall system efficiency and passenger satisfaction. - Optimized resource allocation: resources are used efficiently to accommodate the demand for air travel. - Improved airspace utilization: allows for more flexible routing of flights, particularly during peak demand periods or in response to changing conditions. - Adaptive and dynamic solutions: important for addressing realtime changes, such as weather disruptions. - Cost reduction and resource optimization: This includes minimizing fuel consumption and operational costs. - Enhanced safety and compliance: This ensures that the recommended solutions are not only efficient but also compliant with safety standards.

You can look at different and several objective and KPI all together to evaluate your system. You can use smaller models for different pieces of your system, then tie them together in larger model and see how each affect the other positively or negative

I see lot many valuable comments by other notable LinkedIn members, Thanks for the insight. OR can be used many ways; looking back to the early days of OR, it truly has helped many fighting forces (mainly Britishers) during the WW2 war in many ways. For modern days traffic issues, Operation Research can provide valuable insights and tools for optimizing route and traffic flow in cities, reducing congestion, and improving the overall quality of urban transportation systems. By using data-driven approaches and various modeling techniques, cities can make more informed decisions about managing traffic and infrastructure. I think the usage of mathematical modelling to ease our problems are eroded these days. Can we get it back?

Operations research (OR), which applies analytical and quantitative techniques to optimize transportation networks, can significantly contribute to better traffic flow in urban areas. Here are some of the potential alternatives. Through the integration of below cited OR methods, cities can establish transportation systems that are more intelligent and adaptable, resulting in enhanced traffic flow, decreased congestion, and an urban environment that is more sustainable and efficient. .......................To be Continued

Of courses there are many OR journals and some transportation specific ones, but most major cities use OR in their traffic planning and control. So, if you go to their website, often times you’ll find OR solution they have implemented

Federal Highway Administration (FHWA), the National Operations Center of Excellence, and the American Association of State Highway & Transportation Officials (AASHTO) all work toward knowledge sharing and workforce development using principles of OR. The AASHTO Transportation Operations Manual is a recently develop reference publication that combines many research and OR synthesis projects into a centralized tool for practitioners.

If you want to know the most recent research and advances on how to approach the ATFM, you can search on scholar google the keywords "ATFM optimization". There is a lot of research about it. My recommendation is to start with the surveys so you can have a better context of what has been done and the current techniques. Remember to look for the most cited papers.

Apart from many standard problems like routing, scheduling and traffic load optimisation during peak time. " OR" is quite capable of handling traffic wait time and alternate route suggestions to reduce net time of travel, especially in a City like Bangalore, where driving is the most painful job. Optimisation at several levels based on real time feed through camera installed almost every big junction can play a major role in streaming real time data, which can be easily quantified using a comp. Vision algos and same can be used to derive quantified decision making and suggestions based on the recommendation. It can be fully automated and have an algo driven decision to Google map or similar mapping services.

Operational Research could optimize traffic routes that guarantees daily transport demand during peaks hours. However OR models must consider human behavior variables that incentives people to distribute, commute, do home office and other action. Then reducing congestions, pollutions, stress and increase productive and spare time would be possible to achieve.

The fundamental point of operational research / management science is that those responsible for operations in the organisation being investigated should take ownership of the solutions proposed, implement them, monitor and adjust as necessary. The classic courses in OR and Management Science by and large teach techniques that generally originated in a world without the ICT we have today. Their successes for practitioners were to problems being described for the first time in scientific terms and ingenuity in structuring those problems to be susceptible to new analytic techniques. Major advances came from scientific teams set up by all combatants before and during the second world war taking their knowledge to major firms and Institutes.

Do not reinvent the wheel and do a research to find current ways to solve the problems that you are facing. Ask to experts by direct messages on LinkedIn, researchgate, email, etc. There are always people willing to help.