Analyzing the Effect of Autonomous Ridehailing on Transit Ridership: Competitor or Desirable First-/Last-Mile Connection?

Ridehailing services (e.g., Uber or Lyft) may serve as a substitute or a complement—or some combination thereof—to transit. Automation as an emerging technology is expected to further complicate the current complex relationship between transit and ridehailing. This paper aims to explore how US commuters’ stated willingness to ride transit is influenced by the price of ridehailing services and whether the service is provided by an autonomous vehicle. To that end, a stated preference survey was launched around the US to ask 1,500 commuters how they would choose their commute mode from among choices including their current mode and other conventional modes as well as asking them to choose between their current mode and an autonomous mode. Using a joint stated and revealed preference dataset, a mixed logit model was developed and analyzed. The results show that ridehailing per se might not be a significant competitor to transit, especially if it is integrated with transit as a first-/last-mile service. The total share of transit (transit-only riders plus those who use transit in connection with first-/last-mile ridehailing) remains substantially flat as set against conventional ridehailing services, even if ridehailing fares decrease. On the other hand, when the ridehailing price is significantly reduced by automation, our analysis suggests a decline in total transit ridership and an increase in ridehailing, especially for solo ridehailing. Also, it was found that autonomous pooled ridehailing might not be as appealing to commuters as autonomous solo ridehailing.

Mixed logit model based on nonlinear random utility functions: A transfer passenger demand prediction method on overnight D-trains

In recent years, with the development of high-speed railway in China, the operating mileage and passenger transport capacity have increased rapidly. Due to the high density of trains in the daytime, we usually set up skylights at night within 0:00-6:00 on high-speed railway for comprehensive maintenance, which contradict with the operation demand of D-series overnight high-speed trains (overnight D-trains for short). In order to dynamically adjust the operation plan of overnight D-trains with skylights coordinately, it is necessary to predict the passenger demand of newly-added overnight D-trains. Therefore, the purpose of this paper is to predict transfer passenger demand by formulating a mixed logit model based on nonlinear random utility functions for different transport modes. Firstly according to Maximum Simulated Likelihood Method, the likelihood function of this mixed logit model is proposed to maximize the overall utility value of different passenger groups. And then we adopt Metropolis-Hastings Algorithm to iteratively solve the probabilities of discrete random variables in utility functions. After that, we estimate the unknown distributions of elements in parameter vectors and solve the optimal solution of this model by traditional algorithms, basic heuristic algorithms and improved heuristic algorithms including Imporved Fireworks-Simulated Annealing Algorithm which is proposed in this paper, respectively. Finally, a real-world instance with related data of Beijing-Shanghai corridor, is implemented to demonstrate the performance and effectiveness of the proposed approaches.

Economic Valuation of Local Environmental Amenities: A Case Study of Bahir Dar City, Amhara Regional State, Ethiopia

This study estimates the economic value of local environmental amenities in Bahir Dar city which is one of the tourist attraction sites in Ethiopia. The study employed choice experiment valuation method by identifying four environmental amenities attributes (Lake Tana, urban park, palm tree and street cleanliness). The study used probability multi-stage random sampling technique. The analysis was based on primary data surveyed from households in Bahir Dar city. The study presented nine choices set for each respondent; each choice set has three alternatives including the status quo option. The study employed a mixed logit model. The result showed that all improved attribute levels have positive signs and statistically significant. As expected and consistent with economic theory the monetary cost has negative signs and significant. The mixed logit model showed that there is preference heterogeneity in some attribute levels. Based on the finding, the study recommends that the city administration and the concerned body expected to implement the hypothetical policy scenario so as to improve environmental amenity.

Exploring the Effect of Bicycle Infrastructure on Car Usage: A Case Study in Huhhot, China

This study aimed to quantitatively investigate the effect of bicycle infrastructure on car usage. The mixed logit model with random coefficients was used to capture the differences in individuals’ preferences. Based on data from a stated preference survey conducted in Huhhot, China, the estimated results showed that the mixed logit model provides better fitting than the standard logit model. Considerable variations were found in individuals’ attitudes toward the use of cars and bicycles. Riding a bicycle is preferred by most individuals. Furthermore, based on the constraints for maintaining the effect on car usage equal, the equivalent change in parking fees for improvement in bicycle infrastructures was estimated. The results showed that the effect of a 100 m reduction in walking distance to bicycle stations on the probability of driving is the same as that of an approximately 2.00 yuan/h (US 0.30$/h) increase in the parking fees, and the effect of providing bike lanes is in line with additional parking fees of approximately 3.00 yuan/h (US 0.45$/h). The findings of this study can be an important reference for decision makers to consider improvements in bicycle systems and rational allocation of infrastructure investment and road resources.

How Polling Trends Influence Compensational Coalition-Voting

Compensational voting refers to when voters cast a vote for a more extreme party than they prefer, in order to push policies closer to an ideal point. This article develops the idea of compensational voting in regard to pre-electoral coalition signals and polling trends. The argument is that a significant share of voters consider the relative strength of the parties in their preferred pre-electoral coalition, and adjust their vote choice accordingly. This is elaborated by conducting a mixed logit model over eight Swedish general elections where parties were more or less clear about their intentions to collaborate with other parties. Combining unique data from parties’ election manifestos including negative and positive quotes about other parties with polling trends and voters’ approval rating of parties, the analysis lends support to the idea that this type of coalition-oriented compensational voting occurs.