Modelling of Active and Latent Attributes Based on Traveler Perspectives: Case of Port City of Douala

A growing stream of study stresses the relevance of subjective elements in understanding the hierarchy of preferences that underpin individual travel behavior. The purpose of this study is to evaluate the impact of various factors on mode choice. To achieve this, a multinomial logit model (MNL) was used to analyze the relationships between mode choice and three classes of attributes;Combined Active and Latent, Active only and Latent only attributes. The data used are derived from surveys in the port city of Douala, Cameroon as a case study. Results stipulated that, the combined attributes model performed better than both active only attributes and latent only attributes models. Likewise, latent only attributes model performed better than active only attributes model. The advantage of modelling all three groups is for better selection of the most relevant attributes, and this is very relevant in understanding travel behavior of individuals and mode choice decisions.

Demand Prediction of Ride-Hailing Pick-Up Location Using Ensemble Learning Methods

Ride-hailing and carpooling platforms have become a popular way to move around in urban cities. Based on the principle of matching riders with drivers, with Uber, Lyft and Didi having the largest market share. The challenge remains being able to optimally match rider demand with driver supply, reducing congestion and emissions associated with Vehicle clustering, deadheading, ultimately leading to surge pricing where providers raise the price of the trip in order to attract drivers into such zones. This sudden spike in rates is seen by many riders as disincentive on the service provided. In this paper, data mining techniques are applied to ultimately develop an ensemble learning model based on historical data from City of Chicago Transport provider’s dataset. The objective is to develop a dynamic model capable of predicting rider drop-off location using pick-up location data then subsequently using drop-off location data to predict pick-up points for effective driver deployment under multiple scenarios of privacy and information. Results show neural network algorithms perform best in generalizing pick-up and drop-off points when given only starting point information. Ensemble learning methods, Adaboost and Random forest algorithm are able to predict both drop-off and pick-up points with a MAE of one (1) community area knowing rider pick-up point and Census Tract information only and in reverse predict potential pick-up points using the Drop-off point as the new starting point.

Empirical Analysis of Multiple-Criteria Decision-Making (Mcdm) Process for Freight Transportation Mode Selection

In Ghana, freight transport is growing continuously every year due to its location and business processes. However, road transport carries 86% of freight despite its numerous negative impacts. Hence, the government has invested in rail transport, with 70% of its capacity is for freight transport in her busy freight transport corridor (eastern transport regions of Ghana). Thus, awareness of criteria considered when deciding on freight transport becomes vital. Hence, this study aims to improve the understanding of the factors of freight transport mode selection in Ghana from the decision-making process by identifying criteria that affect their decisions on mode transportation. The combination of Fuzzy AHP and Topsis is used to find the weights and suggest suitable alternatives for the decision-makers in the Eastern transport regions of Ghana. The result of this study shows that the criteria to consider when selecting freight transport mode in the regions are prioritized in other of Transport cost (0.6544), transport Time factors (0.2562), reliability, and flexibility (0.0605), and security, Risk of damage and lose factors (0.0287). Additionally, the suitable mode(s) of transportation in the stated corridor is owned truck carrier transport compared to the railroad, road-barge, and Contracted Carrier, thus, in descending order. The results provide organizations to prioritize these factors when deciding to select freight transport mode. At the same time, the government must remove some inputs that result in high transport costs, enforce policies, and invest in the appropriate mode.

A Research of the Imported Wine Purchasing Strategy for the Medium and Small Size Enterprise

An imported wine purchasing model for medium and small size enterprises is presented based on analytic hierarchy process after analyzing the current purchasing condition of medium and small size enterprise for wine importing. This model is feasible and practical. It will support and assist the purchasing strategy for the enterprise when importing wine within a certain scope and provide the reference to the enterprise for decision-making.

Vehicle adaptive cruise control design with optimal switching between throttle and brake

For vehicle adaptive cruise control （ACC） systems, the switching performance between throttle and brake determines the driving comfort, fuel consumption and service lives of vehicle mechanical components. In this paper, an ACC algorithm with the optimal switching control between throttle and brake is designed in model predictive control （MPC） framework. By introducing the binary integer variables, the dynamics of throttle and brake are integrated in one model expression for the controller design. Then the ACC algorithm is designed to satisfy not only safe car following, but also the optimal switching between throttle and brake, which leads to an online mixed integer quadratic programming solved by the nested two-loop method. The simulation results show that the proposed ACC algorithm meets the requirements of safe car following, outperforms the traditional algorithms by performing smoother responses, reducing the switching times between throttle and brake, and therefore improves driving comfort and fuel efficiency significantly.