One of the most crucial services in modern society is transportation. For any industrialized country, transport modeling is a topic of spatial relevance due to the increasing need for human mobility, which is mostly driven by the geographic disparities between the sites that people need access to. How people move or their mobility patterns are one of the primary issues to be examined. The appropriate planning of the transportation system influences customer behavior as people should arrive at the purchasing destination. Every day, the individual must choose between various alternatives for goods or services, depending on the characteristics or traits of the various available possibilities. Therefore, making the process of purchasing accessible is crucial to attracting customers.
From my personal experience of purchasing groceries, I can assume that I prefer not using public transport but to walk to the groceries stores near my apartment. However, I think that if the transport is accessible, I would use it to arrive at services and other types of purchasing other than groceries. I believe that a strong transportation system is the foundation of any economy. It strengthens links between individuals, promotes wealth, lessens environmental effects, and revitalizes neighborhood communities. Moreover, analyzing different transportation strategies is critical to successful business management.
In my professional experience, there is an example of the significance of a proper transportation strategy. I work in the freight forwarding industry, and my company Dynamic Transport Logistics Solutions Inc, provides import shipment services. One of the clients, BTC Power Cebu Inc., imports raw materials for the production of battery chargers in Cebu, Philippines. Delivery time is significant for them, for this reason, they prefer to ship via air shipments using Cathay Pacific with next-day delivery. Another client, Sheridan Marketing Inc, imports industrial and construction products for distribution to their wholesale and retail customers. They prefer shipment via CMA CGM and Evergreen Shipping lines with 12-16 days transit time which is faster than other shipping lines. These examples show that our customer is willing to pay higher freight costs with faster transit time. If shipping the material or product is not urgent, customers sometimes choose to ship it via sea freight.
Modern logistics systems stimulate businesses to increase their competency in order to offer faster services and take market share while enhancing efficiency, cost-effectiveness, agility, sustainability, and resilience. This is due to the complex behavior of potential customers and the rapid growth of demand (Li et al., 2018). An accurate projection of demand in the near and long term is one important component that affects the performance of delivery systems, specifically related to their strategic and tactical decisions. In addition to the volume of demand, it is crucial to comprehend and anticipate the demands of consumers who have opted for more specialized and tailored delivery services over the past ten years. Therefore, it is believed that demand scenario analysis and consumer behavior modeling are essential for maintaining the robustness and responsiveness of logistic networks in the face of variable demand.
Customized campaigns originated in the 1970s from “realistic choice theory.” The idea is that the majority of people have mobility habits that they believe are impossible to modify (Pasharibu et al., 2018). Therefore, it is cost-effective to identify people who exhibit adaptability and to concentrate resources on this group with information that is tailored to each person’s needs. In any given population, certain individuals are more prone than others to altering their travel habits (Pasharibu et al., 2018). This has something to do with more ethereal elements like their attitudes and impressions about their present travel choices. Some persons face more objective obstacles to modal shift, such as when there is no bus service available along the route of their trip or when they are disabled and unable to move from driving to walking or bicycling (Jaller and Pahwa, 2020). Most of the time, behavior change is seen as a progression of stages that people must pass through in order to get to the end stage, which is a new regular behavior (Fu and Juan, 2017). As a result, subtler behavioral changes will take place concurrently with more overt ones in attitudes and perceptions toward alternative modes (indicating a greater willingness to change behavior).
Evaluations that concentrate only on actual behavior change would not show this, and therefore their success in influencing people toward behavior change would be understated. In order to get a “fuller picture” of what intervention has achieved, it is important to measure these changes in attitudes and perceptions as well as overt behavior change per se (Kim et al., 2018). Before a project begins, measurements of people’s positions on this scale of potential change can therefore be used to inform the choice and design of the following measures, which may give the “last push” necessary to achieve the ultimate objective of actual behavior change (Kim et al., 2018). For instance, a campaign to raise public knowledge about travel options through the media can encourage more people to use them. Although further initiatives (such as tailored travel advice or lower costs) could be needed before people actually move to other modes, this new information may stimulate people to start contemplating the possibilities of using these alternatives.
The MaxSumo approach is a common framework supporting the behavior modification process in Europe. This was created by a group of specialists and has been used by many nations and localities to help define their behavior change initiatives. It splits up the complex behavior change process into smaller steps, making forecasting and analysis more accurate and specific. This approach describes a person’s preparedness to switch their mode of transportation by classifying them into one of four stages: In order to map the evolution of people to later phases of preparedness to change behavior, several cities have found it helpful to ask for a standard set of questions before and after interventions are created and delivered (Bidoni and Montreuil, 2021). A specific intervention’s appropriateness and relevance can then be assessed.
MaxSumo ‘s assessment technique is built on the concept of measuring impacts at several levels. Targets, indicators, and outcomes may be provided at each of these levels, allowing each to be tracked and assessed independently. This allows for the measurement of impacts at an early stage in a project. MaxSumo ‘s design is straightforward, and the methodologies offered are distinct from other transportation and policy evaluation recommendations. It suggests step-by-step directions so that projects are effectively designed, observed, and assessed.
The common goal of these interventions is to encourage environmentally friendly transport while controlling the demand for automobile use. Typically, this will involve either “soft” measures like information and communication campaigns, service organization, and coordinating the efforts of various partners, or “hard” measures like new footpaths or bike lanes within urban transportation, safer crossings, or investment in more comfortable public space (Bidoni and Montreuil, 2021). The effectiveness of “soft” measures can be increased by “hard” measures, but “soft” methods are frequently less expensive in contrast, which may entice certain organizations to prioritize campaigns over supporting infrastructure investments.
While behavior change may result from the intervention itself, there are many other factors that may affect mobility choices over time. These include personal factors like moving or retiring; characteristics of the transportation system like increased service frequency or new buses; subjective improvements brought on by changes in perception; or other external factors like fuel price increases and parking fee increases too (Han et al., 2017). The scope, goals, and targets of the planned behavior change must therefore be established at the beginning, reviewed periodically, and evaluated at the conclusion before value for money can be measured.
To better understand customer behavior in the context of transportation strategies, monitoring and assessment give the opportunity to compare outcomes with those of comparable programs that have undergone evaluation. Benchmarking enables the accumulation of knowledge that would not otherwise be possible. By sharing experiences, people can learn what has worked in the past as well as what has not. Better measurement, documenting, monitoring, and assessment can give more information on how behavior change is affected. This provides substantially better chances to establish verifiable cause and effect correlations in the long run. On the basis of these, future mobility initiatives’ anticipated outcomes can therefore be calculated and predicted.
References
Bidoni, Z. B., & Montreuil, B. (2021). Enabling scientific assessment of large scale hyperconnected urban parcel logistics: Scenario-based demand and customer behavior modeling. In IIE Annual Conference. Proceedings (pp. 1118-1123). Institute of Industrial and Systems Engineers (IISE).
Fu, X., & Juan, Z. (2017). Understanding public transit use behavior: integration of the theory of planned behavior and the customer satisfaction theory. Transportation, 44(5), 1021-1042.
Han, S., Zhao, L., Chen, K., Luo, Z. W., & Mishra, D. (2017). Appointment scheduling and routing optimization of attended home delivery system with random customer behavior. European Journal of Operational Research, 262(3), 966-980.
Jaller, M., & Pahwa, A. (2020). Evaluating the environmental impacts of online shopping: A behavioral and transportation approach. Transportation Research Part D: Transport and Environment, 80, 102223.
Kim, M. J., Lim, C. H., Lee, C. H., Kim, K. J., Park, Y., & Choi, S. (2018). Approach to service design based on customer behavior data: a case study on eco-driving service design using bus drivers’ behavior data. Service Business, 12(1), 203-227.
Li, L., Bai, Y., Song, Z., Chen, A., & Wu, B. (2018). Public transportation competitiveness analysis based on current passenger loyalty. Transportation Research Part A: Policy and Practice, 113, 213-226.
Pasharibu, Y., Paramita, E. L., & Febrianto, S. (2018). Price, service quality and trust on online transportation towards customer satisfaction. Jurnal Ekonomi dan Bisnis, 21(2), 241-266.