How do you predict transportation demand based on behavior and psychology?

Transportation demand is indeed a complex outcome shaped by a multitude of factors, as mentioned earlier. Additionally, the 21st century introduces several other critical considerations. For example, examining trends related to environmental awareness can illuminate how concerns about climate change influence transportation choices. For instance, data may indicate an increase in electric vehicle adoption as environmental awareness grows. New generations tend to prefer commuting in ways that conserve energy. Age, health, demographics, and the availability of transportation also play crucial roles.

I think perhaps one of the more powerful influences regarding choice of private or public transport, yet to date to be even mentioned, is personal empowerment. It’s oft said that the combustion engine was a big thing, which it was/is, but the private automobile has had further reaching implications. Similar in some ways to the firearm - action at a distance, the extension of self to include their expanded physical presence… road rage, sit behind the wheel and that’s you, preference over pedestrians because, well, you’re just bigger, more important. That’s humanity. And that’s what public transport removes from the individual. Because public commuters are just a bunch on of anonymous workers on their way. Therein lies the biggest battle.

I worked for two cargo airlines. 20 years with Flying Tigers, intl agent import and export plus and 25 years with FedEx import export plus dangerous goods inspecting and loading on aircraft

Other than the objective data available i.e. holiday travel which repeats every year, fear & optimism play a role in the decision-making process. For example, when economists provide their outlook, it shapes the behavior of transportation market-makers - executives who decide to expand travel for events or routine business, or constrict travel accordingly. The same is true for consumers - dire economic predictions & rising prices will affect their willingness to purchase travel or put it off. In some cases, a consumer may choose another mode of trans if possible. The objective data available, based on recurring events & objective historical data form the base model. Planners can then collect and synthesize to adjust based on subjective data

Transportation is a driver of growth in terms of economic activities. Many areas that has embraced light rail as a people mover; has seen very vibrant transit oriented development around the newly built stations. This helps to some degree in alleviating highway and street traffic in high populated areas. In addition other forms of transportation such as air, buses and sea continues to add leverage to the manufacturing and tourism sector. Therefore a well strategized transportation plan that is in unison with many participating stakeholders will be a win win outcome for many regions.

Predicting transportation demand through data analysis extends beyond just understanding traveller behaviour and psychology. Incorporating real-time data from emerging technologies like autonomous vehicles, ride-sharing platforms, and smart city infrastructure offers a fresh perspective. These sources provide invaluable insights into evolving mobility patterns, congestion management, and the potential for optimizing transportation services. Harnessing these innovative data streams alongside traditional methods ensures a holistic view of transportation demand, enabling more agile and effective planning and management.

Know your customer, know your transportation supplier, know your environment, know the demand and supply cycles. Will go a long way.

Looking at transportation demand from a fresh angle, we can draw parallels with complex living ecosystems. Just as ecosystems experience shifts and interactions, transportation demand is shaped by intricate relationships among travellers, transport options, and urban settings. To grasp these dynamics, we should embrace a comprehensive, ecological mindset. There are always variables that will come creeping in no matter how stable our predictions are. Transportation demand isn't merely a result of individual choices but a collective, emergent phenomenon. When we model transportation demand as an ecosystem, it encourages planners to consider the ripple effects of their decisions and adapt more effectively and avoid mistakes

We need to be very Agile as consumer behaviour and demand is very dynamic, our plans should be so flexible that we can move our self easily to adjust it as only prediction is not going help us in spite of the best predictions lots of last minute changes are required

Imagine predicting transportation demand based on behaviour and psychology as predicting an ever-changing living ecosystem. To succeed as data analysts, we must recognize that the ecosystem is never static. It adapts. As Data analysts, we need to adapt too, along with changing data and scenarios. Utilizing techniques like cross-validation and adaptive models can help us adapt better. As we make these adjustments, we improve our ability to predict the unpredictable. The need to revise and revisit will always remain as the dynamics of life are ever changing.

Communication and engagement with stakeholders is key because it offers us a fresh perspective. A solution to one problem can be a problem in itself for someone else. For example, Plastic, which started as a revolutionary material has proved to be a nightmare for coastal cities and oceans. Engaging stakeholders ensures a decision and solution without biases. It makes us walk closer to a more wholesome solution.

The more connected we can make the transportation experience, the closer we can get to the demand requirements. This will involve the collection of data across all aspects, from seasonality, weather forecasts, city/country connectivity through to social and economical demographics. The advent of things like artificial intelligence (AI) and machine learning (ML) will allow for the consumption and analysis of huge data sets to model and make big changes rapidly. Design Thinking will help designs be made with the user experience in mind, which should be essential to driving demand and provide a continuous feedback loop.

When you talk about "transportation", then there is a big difference between moving the population around in a city area or between regions/countries AND moving consumer products into or between the same areas for consumption. The last topic is an important factor to dig into also, due to sustainability pressure from consumers and their choices and preferences in buying the next product or everyday groceries from supermarkets.

There are better, more efficient metrics than behavior and psychology. We already have so much data on passenger travel I imagine AI and ML will be sufficient to analyze trends and highlight bottlenecks that, if overwhelmed, might reverberate throughout the transportation system. Interesting behavioral patterns may be inferred from the data, so too psychological motivations. It is far easier to cross-reference passenger movement with times of the day, dates in the year, weather, holidays, phases of the moon, and any other potentially correlating data to find a pattern than it is to predict transportation demand from behavior and psychology. The reverse is a more interesting question.