Research on Congestion Pricing in Multimode Traffic considering Delay and Emission

Rapid development of urbanization and automation has resulted in serious urban traffic congestion and air pollution problems in many Chinese cities recently. As a traffic demand management strategy, congestion pricing is acknowledged to be effective in alleviating the traffic congestion and improving the efficiency of traffic system. This paper proposes an urban traffic congestion pricing model based on the consideration of transportation network efficiency and environment effects. First, the congestion pricing problem under multimode (i.e., car mode and bus mode) urban traffic network condition is investigated. Second, a traffic congestion pricing model based on bilevel programming is formulated for a dual-mode urban transportation network, in which the delay and emission of vehicles are considered. Third, an improved mathematical algorithm combining successive average method with the genetic algorithm is proposed to solve the bilevel programming problem. Finally, a numerical experiment based on a hypothetical network is performed to validate the proposed congestion pricing model and algorithm

A Study on High-Speed Rail Pricing Strategy in the Context of Modes Competition

High-speed rail (HSR) has developed rapidly in China over the recent years, for the less pollution, faster speed, comfort, and safety. However, there is still an issue on how to improve the seat occupancy rates for some HSR lines. This research analyzes the pricing strategy for HSR in Wuhan-Guangzhou corridor based on the competition among different transport modes with the aim of improving occupancy rates. It starts with the theoretical analysis of relationship between market share and ticket fare, and then disaggregate choice models with nested structure based on stated preference (SP) data are established to obtain the market share of HSR under specific ticket fare. Finally, a pricing strategy is proposed to improve the occupancy rates for Wuhan-Guangzhou HSR. The results confirm that a pricing strategy with floating fare should be accepted to improve the profit of HSR; to be specific, the ticket fare should be set in lower level on weekdays and higher level on holidays

Research on Congestion Pricing in Multimode Traffic considering Delay and Emission

Rapid development of urbanization and automation has resulted in serious urban traffic congestion and air pollution problems in many Chinese cities recently. As a traffic demand management strategy, congestion pricing is acknowledged to be effective in alleviating the traffic congestion and improving the efficiency of traffic system. This paper proposes an urban traffic congestion pricing model based on the consideration of transportation network efficiency and environment effects. First, the congestion pricing problem under multimode (i.e., car mode and bus mode) urban traffic network condition is investigated. Second, a traffic congestion pricing model based on bilevel programming is formulated for a dual-mode urban transportation network, in which the delay and emission of vehicles are considered. Third, an improved mathematical algorithm combining successive average method with the genetic algorithm is proposed to solve the bilevel programming problem. Finally, a numerical experiment based on a hypothetical network is performed to validate the proposed congestion pricing model and algorithm. Document type: Articl

Research on Assessment Methods for Urban Public Transport Development in China

In recent years, with the rapid increase in urban population, the urban travel demands in Chinese cities have been increasing dramatically. As a result, developing comprehensive urban transport systems becomes an inevitable choice to meet the growing urban travel demands. In urban transport systems, public transport plays the leading role to promote sustainable urban development. This paper aims to establish an assessment index system for the development level of urban public transport consisting of a target layer, a criterion layer, and an index layer. Review on existing literature shows that methods used in evaluating urban public transport structure are dominantly qualitative. To overcome this shortcoming, fuzzy mathematics method is used for describing qualitative issues quantitatively, and AHP (analytic hierarchy process) is used to quantify expert’s subjective judgment. The assessment model is established based on the fuzzy AHP. The weight of each index is determined through the AHP and the degree of membership of each index through the fuzzy assessment method to obtain the fuzzy synthetic assessment matrix. Finally, a case study is conducted to verify the rationality and practicability of the assessment system and the proposed assessment method

Demand-responsive passenger flow control strategies for metro networks considering service fairness and passengers’ behavioural responses

This paper presents a methodology for developing demand-responsive passenger flow control strategies for oversaturated metro networks. A stated preference-off-revealed preference survey was conducted to capture passengers’ behavioural responses to the passenger flow control strategies, and revealed significant behavioural change intentions in terms of departure times and mode choices. Currently, such behavioural responses are often ignored, leading to deviations in estimating the size of a target group and performance of a proposed strategy. To address this issue, in this study, a mathematical programming with equilibrium constraints (MPEC) approach was developed to optimise demand-responsive passenger flow control strategies. The proposed MPEC model highlights the importance of balancing the operational efficiency of a metro system with service fairness perceived by passengers. In particular, a nested logit-based stochastic user equilibrium problem was included to accommodate potential changes in demand patterns driven by candidate passenger flow control strategies. Two empirical cases based on the Guangzhou and Beijing metros were used to demonstrate the effectiveness of the proposed model and solution algorithm. The results show that the inclusion of service fairness considerations does not contradict the pursuit of efficiency. Instead, dual emphases on service fairness and passengers’ behavioural responses help create a win–win situation for both metro operators and passengers. In the Guangzhou and Beijing metros, the highest section load rate decreased from 126.6% to 105.7% and from 122.98% to 106.87%, respectively, under the optimal passenger flow control strategies. The corresponding network load Gini coefficients improved from 0.278 to 0.259 and from 0.269 to 0.248, demonstrating the remarkable performance of this approach in regards to peak cutting and load balancing

Joint modeling of mode choice and travel distance with intra-household interactions

Travel mode and distance choices are not independent decisions, and individual choices are usually made in the knowledge of the preference and needs of other household members. To represent the dependency between mode choice and travel distance and the role of intra-household interactions in travel behavior, we develop a joint discrete–continuous model with intra-household interactions. In this model, we use joint household tour as the unit of analysis and characterize the dependency between mode choice and travel distance using flexible copula functions. Then, we apply the proposed model to analyze tour-based mode choice and travel distance in Beijing, China. The results indicate that unobserved factors contribute to positive dependency between mode choice and travel distance. The choice of walk is more dependent on travel distance than other travel modes, and the choice of travel modes except public transit shows a higher correlation with travel distance in complex individual tours than in simple individual and joint household tours. Further, a comparison between the proposed model, the independent model, and the model without intra-household interactions reveals that ignoring the dependency between mode choice and travel distance, or not considering intra-household interactions, could lead to over- or under-estimation of the effects of changes in exogenous variables

Optimizing EV-based P&R subsidy policies for commuting corridor based on cross-nested logit model

The promotion of electric vehicles (EVs) is restricted by cruising range limitation and charging station deficiency. Given the mature development of Park and Ride (P&R) mode, which is used in many cities worldwide to attract more travelers to use public transit, a new travel mode, i.e., EV-based P&R is introduced as an alternative for commuters’ daily travel. This seems quite attractive to expand the use of EVs and further increase their market share. This paper aims to investigate the impact of EV-based P&R introduction on travel mode choice along commuting corridor, and further aid in the optimal subsidy policies decision for the government. A bi-level model is proposed to model the presented problem. The lower level describes commuters’ joint mode and transfer choice behavior through a cross-nested logit (CNL) model, while the upper level minimizes the system cost. A genetic algorithm is developed to solve the formulated model with a partial linearization algorithm for solving the lower level model. And a numerical example is then used to demonstrate the effectiveness of the methodology and illustrate how the network flow pattern reshapes due to the introduction of EVs into the P&R mode and the change of corresponding subsidy policies. As the results show, improving the EV hardware, applying the intelligent supporting service system, developing new technologies for EV fast charging, appropriately improving the parking space capacity, and increasing the parking fee of transfer stations near the central business district (CBD) are all helpful to save the social cost and promote the usage of EVs

Does Urban Rail Transit Discourage People from Owning and Using Cars? Evidence from Beijing, China

With the rapid urbanization and motorization, many cities are developing urban rail transit (URT) to reduce car dependence. This paper explores the URT effect on car ownership and use based on the home-based work tour data in Beijing, China. Considering the mediating effects of car ownership and travel distance simultaneously, we develop a structural equation model to examine the complex relationship among URT, car ownership, travel distance, and car use. The results indicate that URT plays an important role in reducing car dependence. Living within URT catchment areas by itself is not significantly associated with car ownership and use, but if the workplace is near a URT station, people are less likely to own and use cars. People who both live and work near URT station areas have lower probability of owning and using cars. Moreover, car ownership and travel distance mediate the relationship between URT and car use, and the mediating effect of car ownership is greater than travel distance. Our study verifies that URT does discourage people from owning and using cars, which may have important implications for developing cities to make response to the ongoing motorization

Exploring urban rail transit station-level ridership growth with network expansion

Urban rail transit (URT) is experiencing rapid network expansion in metropolises in China. The network expansion not only improves accessibility, but also motivates the surrounding land use development, which may have important effects on the ridership of existing stations. Considering the network scale, station-level accessibility increment, station characteristics, etc., this study develops an approach to explore the URT station-level ridership growth with network expansion, which can provide transit resource allocation guidance for URT agencies. Instead of collecting land use and socioeconomic data with huge labor and cost, we make good use of Automatic Fare Collection (AFC) data to develop proxy variables. Based on the temporal distribution of station-level ridership, a proxy for land use type is proposed. Then, multiple explanatory variables representing network expansion are introduced, and further the multivariate regression models are established to explore station-level ridership growth with network topology change and surrounding land use development. The results show that the proposed approach has good abilities to explain station-level ridership growth with network expansion and can make a response to network topology change and surrounding land use change

Degradable transportation network with the addition of electric vehicles: Network equilibrium analysis.

Introducing electric vehicles (EVs) into urban transportation network brings higher requirement on travel time reliability and charging reliability. Specifically, it is believed that travel time reliability is a key factor influencing travelers' route choice. Meanwhile, due to the limited cruising range, EV drivers need to better learn about the required energy for the whole trip to make decisions about whether charging or not and where to charge (i.e., charging reliability). Since EV energy consumption is highly related to travel speed, network uncertainty affects travel time and charging demand estimation significantly. Considering the network uncertainty resulted from link degradation, which influences the distribution of travel demand on transportation network and the energy demand on power network, this paper aims to develop a reliability-based network equilibrium framework for accommodating degradable road conditions with the addition of EVs. First, based on the link travel time distribution, the mean and variance of route travel time and monetary expenses related to energy consumption are deduced, respectively. And the charging time distribution of EVs with charging demand is also estimated. Then, a nested structure is considered to deal with the difference of route choice behavior derived by the different uncertainty degrees between the routes with and without degradable links. Given the expected generalized travel cost and a psychological safety margin, a traffic assignment model with the addition of EVs is formulated. Subsequently, a heuristic solution algorithm is developed to solve the proposed model. Finally, the effects of travelers' risk attitude, network degradation degree, and EV penetration rate on network performance are illustrated through an example network. The numerical results show that the difference of travelers' risk attitudes does have impact on the route choice, and the widespread adoption of EVs can cut down the total system travel cost effectively when the transportation network is more reliable