The customized bus (CB) is an alternative public transportation mode that extends the flexibility and coverage of the fixed-route transit networks. It allows passengers to make reservations for trips and arranges vehicles to serve shared rides. However, the operation performance is limited to vehicle routing, the number of stops, and the length of detour times. Extra detours and stops would not be significantly avoided if deploying high-capacity vehicles. The demand control aspect is a possible way out. Releasing incentives to passengers can attract them to aggregated locations and reduce the vehicle detour times. How to determine an appropriate incentive scheme for passengers is the critical problem. This paper presents an approach to integrating the disaggregated trip choice model with the vehicle routing model to determine incentive schemes. First, the discrete choice model is established to bridge the passengers’ trip choice probabilities with the influence of monetary incentives, walking time, and travel time. A vehicle routing model based on the pickups and deliveries problem is then adopted to generate the routes and schedules of vehicles to serve the influenced passengers. The result shows that the proposed approach can reduce the total running kilometers, shorten the onboard time, and increase the profits. The analysis also suggests that passengers’ sensitivity towards incentives is decisive to the result.

As the service is becoming the strategic preference of the manufacturing companies, bullwhip effect caused by customers’ baulking behaviours is a pervasive and enduring characteristic of supply chain management to be considered. Moreover, the interaction between customers’ baulking behaviours and capacity utilisation also has a great impact on bullwhip effect, which draws an important concern in supply chain research. To tackle this problem, we establish a system dynamics model and make a case study on a tool manufacturer in Shanghai, China. The customers’ baulking behaviours are represented as customers’ responses to variable service levels in this research. Firstly, the model is validated by comparing simulation results with practical data. Then, the oscillation characteristics of stochastic demand information distortions caused by customers’ baulking behaviours and capacity utilisation are analysed. Lastly, we show how the inventory and responsive utilisation strategies interact with each other in smoothing the bullwhip effect under such demand pattern. The model yields insights into the suitable inventory and responsive utilisation strategies with customer response to supply availability.

Pricing directly affects the sustainable development of the flexible bus service. This study proposes a profit maximization model and a social welfare maximization model for the flexible bus operator based on the cumulative prospect theory. Fares and uncertain travel time due to unforeseen detours in serving passengers jointly affect passengers’ mode choice. On the other hand, fares and passengers’ probabilistic choices over the flexible bus jointly determine the profits of the flexible bus company and social welfare. This study explores the relationship between the probability of passengers choosing the flexible bus, trip fares, and uncertain travel time. Serving more passengers indicates more profits, which also results in longer detour time thus decreasing the probability of passengers choosing the flexible bus. Considering the interactive influence among passengers, we further calculate the detour time distribution. Finally, a pricing model is established to compensate for the side effects of the detour. The results show that heterogeneous fares can help the flexible bus company to obtain higher profits but have negligible influence on social welfare. In addition, the development of long-distance services and regulations over the detour time can also help to obtain more profits.

Travel trajectories of individuals, collected by smartphone sensors, carry substantial trip chain information on advanced urban transportation operation. Automatic information extraction plays a critical role in intelligent transportation data collection and analysis, with higher efficiency and accuracy. This research proposes an entire trip chain extraction procedure, including integrated activity/trip segmentation and travel-mode and activity-type recognition components. Specifically, a unique sliding-window Euclidean distance method is proposed to segment trajectories into travel and activity segments. Four alternative black-box pattern recognition (PR) methods, including decision trees (DT), multilayer perceptrons (MLP), radial basis function neural networks (RBFNN), and support vector machines (SVM), are used to classify travel-mode and activity type. An F-Score-based feature-selecting rule is further introduced to improve the performance of the travel-mode/activity-type classifiers. The proposed extraction procedure is demonstrated using trajectory data collected from volunteers. Test results show that the proposed sliding-window Euclidean distance segmentation approach has error rates as low as 2–4%; recognition tests suggest that SVM performs the best in travel-mode recognition and MLP performs the best in activity-type recognition; feature selection tests reveal that the methods with feature selection achieve higher accuracy than those with total features. Overall, the proposed trip chain extraction procedure achieves an average completion rate above 80%.

On‐demand one‐way carsharing systems are increasingly gaining popularity nowadays and the market is growing unprecedentedly. However, operating techniques such as vehicle surveillance and fleet management fall behind the industrial development. Practice on EVCARD – an on‐demand one‐way electric vehicle carsharing system operating in Shanghai – raises an issue on dynamically predicting the user destinations, in order to support decisions on dynamic fleet management. This study presents a global positioning system (GPS)‐data‐driven method to solve the problem. The historical vehicle GPS data is enabled to match the user current trajectories and infer their possible destinations. Based on the GPS trajectory similarity measurement, the study presents a four‐step procedure, including (i) similarity calculation, (ii) most‐similar track detection, (iii) adjustment, and (iv) sorting. The method also takes the station correlations and the user historical destinations into account. A case study is given to demonstrate the dynamic prediction procedure of this method. Experiment on 96,821 valid test tracks shows that the positive prediction rate can be above 92% if the test trip has been completed over 70%. Factors that may influence the prediction result are additionally discussed, which include the existence of round‐trips, the coverage of samples, and the alternatives of destinations.