With the rapid aging population, the number of elderly passengers using bus services for daily travel will increase. Thus, it is increasingly more important for bus companies to understand the specific service requirements and expectations of elderly passengers. However, we are not aware of any dedicated studies on service requirement and travel satisfaction of the elderly. As an inclusive society is considered a key aspect of sustainable development, guaranteeing travel satisfaction is a sine qua non for sustainable transportation. Therefore, this study develops a customer satisfaction index (CSI) for the elderly. Assuming that perceived service quality affects passenger satisfaction, a structural equation model (SEM) is estimated to derive the importance of 10 dimensions of perceived service quality: reliability, time schedule, route characteristics, ticketing, driver service, convenience, safety and security, cleanliness, comfort, and information services. Furthermore, the effect of satisfaction on loyalty and filing complaints against the service company is examined. SEM is developed using data from a bus passenger survey, administered in 2017 in Harbin, China. The results show while some service dimensions such as time schedule and reliability are less important to the elderly (possibly due to their less strict time constraints) other dimensions such as service and security, convenience and driver service are in need of improvement due to the fact that they are very important for the elderly but their perceived performance from the elderly's perspective is low.

In frequently serviced bus routes passengers are more concerned about bus headway regularity than actual punctuality to the schedule. The buses tend to bunch naturally partly because of the participation of passengers in the system. Thus, the irregularity of headway of routes with high passenger demand is extremely serious. With the development of technique, bus realtime information containing arrival times and passenger-loads of following buses can be provided for passengers. A special ''control way'' which is particularly adaptive to the high-frequency routes is proposed based on this background. It is thought that passengers would control the headway regularity themselves when provided with bus real-time information. To test the effects of this special ''control way,'' bus-focused stochastic models are used for the simulation. The results show that providing real-time information does improve headway regularity as expected. The impacts are significant in morning-peak simulation. There is a reduction of 30.1% of headway irregularity after providing real-time information in morning peak, as well as, 18.5% in off-peak. Besides, through the comparison of line without passenger and normal line, it is confirmed that passengers are a vital factor contributing to the headway irregularity. By providing real-time information, the moderating effects of passengers approximately offset the negative effects from them both in morning peak and in off-peak. Lastly, as expected, passenger loads among buses became more even both in morning peak and in off-peak.

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Integrated transportation is one of the most important methods to encourage the modal shift from car to public transportation (PT). However, as most cities have an existing multimodal network, it is difficult to expand the current networks by building more PT routes. Thus, integrating different modes through the optimization of hubs is a cost-efficient way to promote sustainable mobility. This paper develops a bilevel multimodal network design problem based on the collaborative optimization of urban transportation hubs. The upper-level problem is formulated as a mixed-integer nonlinear program to achieve a modal shift from congested subnetworks to underutilized subnetworks to realize a balanced use of the entire network. The decision variables are classified into location-based (hub locations) and route-based (route layouts and frequency setting) ones. The lower-level problem is a generalized modal split/traffic assignment problem (GMS/TAP), which captures the mode choices of all modes in the path set. The GMS/TAP is formulated as a nonlinear optimization problem (NLP) and is solved using a hybrid method of the successive average (MSA) algorithm. A hybrid genetic search with advanced diversity control (HGSADC) is developed to solve the bilevel model, where the exploration of the search space is expanded using the biased fitness function and diversification mechanism. The solution properties of the hybrid MSA and HGSADC are demonstrated in two modified nine-node networks. The model performance is illustrated in a real-size network in Jianye district, Nanjing. 9.2% decrease of travel time, 25.7% increase of service level, and a significant modal shift from car to PT are obtained.