Managing complex travel patterns is a key challenge for transportation planners, who need to understand how people move, why they choose certain modes and routes, and how they respond to changes in the environment and policies. In this article, we will explore some of the tools and methods that can help you analyze and influence travel behavior and choice modeling.
The first step in managing complex travel patterns is to collect and analyze data on the characteristics, preferences, and activities of travelers. This can include surveys, interviews, focus groups, GPS tracking, smart cards, mobile apps, and sensors. The data can provide insights into the trip purposes, origins, destinations, modes, times, frequencies, costs, and impacts of travel. You can use various software and techniques to process, visualize, and interpret the data, such as GIS, statistical models, machine learning, and network analysis.
One more important aspect of this phase (data collection and analysis), is the lived experience of the specialist. This can enhance the quality and value of the data collected and analysis.
In public Transit it is so important to do following investigation: 1 - Official Rules and Regulations - how it should be 2 - Observation in the field how it works right now 3 - Interview stakeholders on different layers - Managers, conductors, drivers 4 - Make Passenger Surveys 5 - Try t get access to big data from telecom, transit operators Based on this data you can make full picture of what is happening.
Nowadays The most important item in procedure of data gathering is combination of image processing and AI that really helpful for finding travel patterns especially in sub-networks. In fact localization of travel data most of the time leads to identification of whole network patterns.
These are all relevant tools and approaches. However, their application, relevance and statistical reliability need to be tested prior to method selection. This means, that for an accurate measurement of the complexity, a clear framework is needed to inform the choice of method and process toward this choice. Many researchers rely on expertise or experience to identify an appropriate tool for complex data collection. Sometimes this yields accurate results, that measure the complexity in a way that reveals a degree of confirmation bias. What is important is to design data collection methods and instruments that limit lexography - within the subject, and by the subject matter experts conducting the study.
I personally believe that interviews would not work as a useful tool to gather data because individuals often find it difficult to imagine various modes of transportation. I think the answers to interview questions are not as reliable as the data gathered from evaluating the behaviors of travelers in a network. It might go beyond privacy policies, but having live data from travelers would significantly increase the accuracy of data collection and reduce ambiguities. Surely, using AI tools to evaluate such data would be the most appropriate approach towards the data collection phase.
The next step is to use the data to forecast the future travel demand and its implications for the transportation system. This involves estimating the number and type of trips that will be generated, distributed, and assigned to different modes and routes under various scenarios. You can use different methods and models to forecast travel demand, such as trip-based, activity-based, agent-based, and disaggregate models. These models can help you assess the effects of population growth, land use, economic development, and transportation policies on travel patterns.
One can also use other trusted sources of data to determine traffic volumes. Demand also needs to be mapped with historic actuals to ensure the data is as realistic. Thereafter we can forecast as far as we can.
The third step is to understand and influence the travel behavior and choice modeling of travelers. This involves exploring the factors that affect how people decide where, when, how, and why to travel, and how they react to different incentives and disincentives. You can use different approaches and models to study travel behavior and choice modeling, such as utility theory, discrete choice models, behavioral economics, and social psychology. These models can help you design and evaluate strategies to encourage more sustainable and efficient travel behavior, such as pricing, information, education, and infrastructure.
From a South African perspective, you will find that travel behaviour is not a choice for most of commuters. Public transport is mostly taxis which are more a forced mode than a choice for many. It is important to consider such conditions in data analysis and in designing relevant strategies for a specific region or area.
Based on collected data on daily trips and fare rules, it is recommended to design new routes (e.g. to avoid interchanges), increase capacity of vehicles (e.g. put bigger buses for rush hours and most overloaded routes), change routes (e.g. make express routes with less stops and reduce time in transit), add first last mile (e.g. feeder buses, bike/scooter rent, parking interchange). Also data should be used in taxation and city planning to manage passenger flows on long run and be proactive to reduce traffic and transit time.
The final step is to use the results of the previous steps to plan and evaluate different scenarios for managing complex travel patterns. This involves comparing the performance and impacts of alternative transportation plans, projects, and policies on the travel patterns and outcomes of interest. You can use different tools and methods to plan and evaluate scenarios, such as multi-criteria analysis, cost-benefit analysis, impact assessment, and stakeholder engagement. These tools and methods can help you identify and prioritize the most feasible and desirable solutions for managing complex travel patterns.
Managing complex travel patterns is a dynamic and multidisciplinary process that requires a combination of data, models, tools, and methods. By applying these steps, you can enhance your transportation planning skills and contribute to more effective and sustainable mobility solutions.
In most cases scenario planning tends to be conservative and picks the middle road, therefore, modeling current trajectory. It helps to plan for different scenarios, however, for a critical change researchers need to consider including scenarios that are completely disconnected from the current reality.
Keep in mind time in transit (this help to increase efficiency of public transportation and convenience of urban life), transparency of fare collection systems - to keep industry attractive for investors.
Market study, new approach and proper planning along with finding alternatives modes. Also adopt suggestions from team members.