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Limited-stop bus services are a highly efficient way to release more potential of the public transit system to meet travel demand, especially under constraints on vehicle fleet size and transportation infrastructure. This work first proposes a visualized fare table for the design of limited-stop bus services along a public transit corridor, along which many lines of public transit carry a heavy load of demand back and forth every working day. Based on this proposed fare table, a set of fare strategies and desired aims of fare policy, a differentiated fare structure is established to improve social equity and increase revenue. The nature of the structure can help travellers understand how to be charged between their origins and destinations (e.g. flat, time-based, stop-based or quality-based pricing) and then plan their trips efficiently. Secondly, a model is formulated to minimize the total social cost in designing a fixed demand limited-stop bus service system with a differentiated fare structure. Thirdly, numerical results are carried out with sensitivity analysis within three scenarios of differentiated fare structures. It is found that a differentiated fare structure has a great effect on passenger path choice behaviour and resulting optimal design of bus services. An attractive feature of this differentiated fare structure is that it could not only enhance the operator’s revenue and social equity but also reduce passenger transfers and social cost.

Since 2019, the problem of brain drain of seafarers in China has become more and more serious, which has a negative impact on the healthy and sustainable development of the shipping industry. As shown from the data of ''China Crew Development Report 2019'', the brain drain of seafarers in China is mainly concentrated on the third officers and fourth engineers. Semi-structured interviews and questionnaire surveys were conducted and quantitative and qualitative analysis was carried out from a macroscopic perspective. Main reasons for the brain drain of third officers and fourth engineers include narrowing income gap between “sea and land” work, low social status, difficulty in promotion, insufficient English proficiency, etc. Corresponding improvement measures have been proposed for the government, the competent authority, and maritime colleges and universities with a view to slow down the brain drain of seafarers.

This paper aims to optimize fares and transfer discounts for public transit service along a bus-subway corridor with the consideration of effects of uncertainty in travel times and difference in stop spacing between bus and subway services on passenger behavior. The former factor is captured by the reserved time in travel cost and the latter one produces some passenger Origin–Destination (O–D) pairs along the corridor that can not be served by one mode only. This problem is formulated as a bi-level program, of which the upper level maximizes the social welfare and the lower-level capturing traveler choice behavior is a variable-demand Stochastic User Equilibrium (SUE) assignment model. A Genetic Algorithm (GA) is applied to solve the bi-level program while the Method of Successive Averages (MSA) is adopted to solve the lower-level model. A series of numerical experiments are carried out to illustrate the performance of the model and solution method. Numerical results indicate that the implementation of transfer discounts may be of great benefit to the social welfare and that the uncertainty in travel time and the difference in stop spacing play an important role in determining optimal fares and transfer discounts for the service along a bus-subway corridor.

Formal method plays an important role in modeling large scale concurrent networks through its efficient model construction and analysis. Taking urban road networks and public transportation systems as examples, such models can be defined in a formal method in order to investigate the performance of current bus-line deployment based on a given road network. This paper considers how to efficiently build a formal model based on such an original prototype of the specified road network and transportation system. As the model prototype can be represented by a directed graph that is then transformed into a numerical incidence matrix, we proposed an algorithm, in this paper, to assist the construction of formal models by sorting all potential compositional structures and components based on the previously obtained numerical incidence matrix. Thereafter, a performance evaluation process algebra-based formal model can be automatically generated on the basis of sorted compositional structures, which extends the use of formal method for largescale and comprehensive network, and system modeling. The findings reveal that the proposed algorithm can efficiently find all potential compositional structures that include all potential components and related activity flows in models.

It is nowadays widely accepted that solving traffic congestion from the demand side is more important and more feasible than offering more capacity or facilities for transportation. Following a brief overview of evolution of the concept of Travel Demand Management (TDM), there is a discussion on the TDM foundations that include demand-side strategies, traveler choice and application settings and the new dimensions that ATDM (Active forms of Transportation and Demand Management) bring to TDM, i.e. active management and integrative management. Subsequently, the authors provide a short review of the state-of-the-art TDM focusing on relevant literature published since 2000. Next, we highlight five TDM topics that are currently hot: traffic congestion pricing, public transit and bicycles, travel behavior, travel plans and methodology. The paper closes with some concluding remarks.

At an isolated signalized intersection, short left-turn lanes may be provided to enhance intersection capacity and level of service. The capacity of an entire intersection depends not only on the effective green time per phase and the length of each short left-turn lane but also on the selected signal phase plan. This paper enumerates 16 typical signal phase plans for a four-leg intersection with protected left-turn phases. Given the existence of short left-turn lanes and the freedom to select signal phase plans, two optimization models are formulated. Numerical examples are carried out to illustrate the application of these models and show the sensitivity of the outcomes to impact factors. The orthogonal experiments show that the primary factor is whether a short left-turn lane is added on an approach and the demand distribution is the secondary factor in determining an optimal allocation of the time-space resources of an intersection. To analyse traffic flow operations under different signal phase sequences, the simulation tests are fulfilled using VISSIM under the assumption that the components of a signal phase plan and green splits are all identical. The simulated results indicate that the signal phase sequence for a specified approach may greatly affect traffic movements at an approach, and the leading green phasing is better than the lagging one for a specified approach when the left-turn bay length is short at the approach. Finally, two variations of the developed models are recommended for use and the procedure for the model application is provided in practice. First published online 27 April 2017

Many congestion charging projects charge traffic only within part of a day with predetermined congestion tolls. Demand peaks have been witnessed just around the time when the charge jumps up or down. Such peaks may not be desirable, in particular (a) when the resulting peaks are much higher than available capacities; (b) traffic speeding up to get into the charging zone causes more incidents just before the toll rises up to a higher level; or (c) traffic slowing down or parking on the roadside decreases road traffic throughput just before the toll falls sharply. We term these types of demand peaks ‘boundary effects’ of congestion charging. This paper investigates these effects in a bottleneck scenario and aims to design charging schemes that reduce undesired demand peaks. For this purpose, we observe and analyse the boundary effects utilising a bottleneck model under three types of toll profiles that are indicative of real charging schemes. The first type maintains a constant toll across the charging period, the second type allows the toll to increase from zero to a given maximum level and then decrease back to zero and the third type allows the toll to rise from zero to a given maximum level, remain at this level for a fixed period and then fall down to zero. This investigation shows that all three types of toll profiles can produce greater boundary peak demands than the bottleneck capacity. A significant contribution of this work is that instead of designing an optimal traffic congestion pricing scheme we analyse how existing sub-optimal congestion pricing schemes could be improved and suggest how observed problems may be overcome. Hence, we propose a set of extra requirements to supplement existing principles or requirements for design and implementation of congestion charging, which aim to reduce the adverse consequences of boundary effects. Concluding remarks are made on implications of this investigation for the improvement of existing congestion charging projects and for future research. First published online 13 July 2015

Public transport passenger demand is inevitably made non-uniform because of spatial and temporal land use planning. This non-uniformity warrants the use of public transport operational strategies to attain operating efficiency. The optimization of these strategies is commonly being done from the operator perspective, and indirectly from the user perspective. However, the environmental perspective of these strategies, in terms of vehicle's emission, has not been investigated. This study proposed a methodology to analyze the benefits of using transit operational strategies to reduce operating cost and eventually also to reduce undesirable emissions. First, a strategy-based optimization model is established to minimize the number of transit vehicles required. Four candidate operational strategies are considered in this model, including full route operation (FRO), short turn, limited stop, and a combination of limited stop and short turn. Second, the pollutant emissions of transit vehicles are estimated by the MOVES emission model. The developed methodology is applied to a real life case study in Dalian, China. Results show that the use of operational strategies can not only significantly save the number of vehicles by 12.5%, but also reduce emissions of pollutants (i.e., CO2, HC, CO, NOx, PM2.5) by approximately 13%, compared with applying FRO strategy exclusively. In addition, both benefits can be further enhanced through the use of an efficient payment mode (e.g., off-board or contactless card) or improving bus performance in deceleration/acceleration as well as doors opening and closing at a stop.

This paper aims to optimize a desirable fare structure for the public transit service along a bus-subway corridor with the consideration of those factors related to equity in trip, including travel distance and comfort level. The travel distance factor is represented by the distance-based fare strategy, which is an existing differential strategy. The comfort level one is considered in the area-based fare strategy which is a new differential strategy defined in this paper. Both factors are referred to by the combined fare strategy which is composed of distance-based and area-based fare strategies. The flat fare strategy is applied to determine a reference level of social welfare and obtain the general passenger flow along transit lines, which is used to divide areas or zones along the corridor. This problem is formulated as a bi-level program, of which the upper level maximizes the social welfare and the lower level capturing traveler choice behavior is a variable-demand stochastic user equilibrium assignment model. A genetic algorithm is applied to solve the bi-level program while the method of successive averages is adopted to solve the lower-level model. A series of numerical experiments are carried out to illustrate the performance of the models and solution methods. Numerical results indicate that all three differential fare strategies play a better role in enhancing the social welfare than the flat fare strategy and that the fare structure under the combined fare strategy generates the highest social welfare and the largest resulting passenger demand, which implies that the more equity factors a differential fare strategy involves the more desirable fare structure the strategy has.

An optimization approach for designing a transit service system is proposed. Its objective would be the maximization of total social welfare, by providing a profitable fare structure and tailoring operational strategies to passenger demand. These operational strategies include full route operation (FRO), limited stop, short turn, and a mix of the latter two strategies. The demand function is formulated to reflect the attributes of these strategies, in-vehicle crowding, and fare effects on demand variation. The fare is either a flat fare or a differential fare structure; the latter is based on trip distance and achieved service levels. This proposed methodology is applied to a case study of Dalian, China. The optimal results indicate that an optimal combination of operational strategies integrated with a differential fare structure results in the highest potential for increasing total social welfare, if the value of parameter ε related to additional service fee is low. When this value increases up to more than a threshold, strategies with a flat fare show greater benefits. If this value increases beyond yet another threshold, the use of skipped stop strategies is not recommended.

For decades, the general motors (gm) car following model has received a great deal of attention and provided a basic framework to describe the interactions between vehicles on the road. It is based on the stimulus-response assumption that the following vehicle responds to the relative speed between the lead vehicle and itself. However, some of the empirical findings show that the assumption of gm model is not always true and need some modification. For example, the acceleration of the following vehicle is very sensitive to the sign of the relative speed and because of no term in the model that directly represents the leader’s acceleration, the follower’s response to the leader’s acceleration can be retarded. This paper offers a new car-following model that can be considered as a variant of the gm model that can better capture car following behavior. The new model treats the follower’s acceleration as a proportion of a weighted sum of the leader’s acceleration and the relative speed between the lead and following vehicles. This paper compares the new model with the original gm model numerically and the characteristics of the new parameters in the model are investigated. It is also shown that the new model overcomes the shortcomings of the original gm model identified in this paper and gives us more instruments to capture the real-world car-following behavior. First published online: 28 May 2014

"Travel demand management: short review of the special issue" Transport, 29(3), pp. 233-234

The paper begins with a brief review of evidence related to the different types of ‘green’ taxation measures that have been introduced by governments worldwide as a means to lessen the environmental consequences of private car ownership by incentivising the purchase of Low Emission Vehicles (LEV’s). An overview is then provided of current understanding of individuals’ car purchasing decisions, highlighting gaps in knowledge that exist with current explanatory models. Based on these gaps in knowledge and to investigate the potential of various taxation measures in influencing individuals’ car purchasing decisions, an online survey was conducted with students from Dalian University of Technology, China. The questionnaire was designed to measure: (1) situational and psychological factors that would be important for their next car purchase; (2) the impacts of adapting current taxation measures to incentivise LEV purchases; (3) the potential role of a range of taxation measures on their decisions on type of future car purchases. K-Cluster means analysis was employed to identify population segments according to their psychological preparedness and importance attached to situational factors in their future decisions to purchase a LEV, or not. This resulted in three distinct segments – Pro-Greens (33.6% of sample), Maybe-Greens (20.2%) and No-Greens (46.2%). Pro-Greens were more psychologically prepared to purchase LEV’s than the other two segments, whose future car purchasing decisions were primarily driven by situational factors (vehicle characteristics, performance, etc.). Pro-Greens were also more likely to be influenced by changes in current Chinese taxation measures (VAT and fuel duty), and also significantly more likely to be influenced by the introduction of future measures that would incentivise LEV purchases than the other two segments. In terms of future measures to incentivise future Chinese drivers LEV purchases and use, the introduction of a feebate system, a CO2 emissions VAT system, a distance based user charging scheme and annual road tax based on CO2 emissions would appear to have the greatest potential overall. First published online: 09 May 2014

With the emergence of Level 4 automated vehicles, it is necessary to investigate the impact of these vehicles on mode choice. Previous studies have looked at the potential benefits and drawbacks of automated vehicles, but there has been little research done on how these vehicles will impact individuals' travel behaviors. This paper proposes a needs-based approach to study the impact of Level 4 automated vehicles on mode choice. The approach takes into consideration the travel needs of different individuals and their willingness to adopt new technologies. Through a stated preference survey in China, the data on travel preferences and the perceived safety levels of automated vehicles can be collected. Then, a model is built to simulate the adoption of Level 4 automated vehicles and estimate the mode split for different scenarios. The results indicated that private AV modes are preferred, and business and nonwork trips may be the targeted market for all AV modes. Overall, value of automation increases with income for private modes, with large variance. Furthermore, Pro-AV attitude has a positive effect on value of automation, especially for self-owned AV and AV subscription. Accordingly, the needs-based approach demonstrates a promising method to study the impact of new technologies on travel behaviors and provides insights for policy makers to promote more sustainable transportation systems., Author(s): Chen Zhijian [1,2]; Ge Ying-En [3]; Shikun Liu [4]; Jiandong Qiu (corresponding author) [2,5]; Jingke Zhang [6,7]; Jing Kang [2] 1. Introduction With the emergence of automated vehicles [1-5], [...]