Personal tradable carbon permits for road transport: Why, why not and who wins?

Personal road transport sector poses a significant challenge in reducing carbon emissions. This paper evaluates a policy approach known as personal tradable carbon permits to reduce carbon emissions from personal vehicles. The policy is a downstream tradable permit where individuals are allocated carbon emission caps. The policy is qualitatively evaluated in the context of carbon taxes and some upstream tradable permit options. The biggest disadvantage of such a policy is the initial set up costs. Personal tradable permits, however, are more effective than carbon taxes and are also capable of stabilizing the gasoline prices faced by the consumers when the underlying oil prices fluctuate. Since equity effects are often a concern to policy makers, the effect of such personal carbon permits on the distribution of burden is quantified in a partial equilibrium framework for the US population. Different permit allocation strategies are investigated in this regard. Using US consumer expenditure survey data, and incorporating a differentiated price response for different households, we find that all three allocation strategies considered are progressive: a per adult based allocation is the most progressive, a per vehicle allocation nearer to proportional, and a per capita allocation in between the two. Personal tradable permits therefore take care of equity concerns directly through the design of the policy.     

The use of transferable permits in transport policy

This paper considers potential use of domestic transferable, or tradable, permit systems for the purposes of travel management, especially reducing environmental nuisances. The main arguments for and against the use of permits are analyzed. Secondly two case studies of existing permit systems are examined. The main conclusions are that tradable permits can address greenhouse gas and regional atmospheric pollutant emissions, and are suitable for congestion on a restricted time–space basis. Permits applied to mobile sources are technically feasible at acceptable financial cost for protecting sensitive geographical areas, and schemes applied to automakers for unitary vehicle emissions are also viable.     

Trading mechanisms for bottleneck permits with multiple purchase opportunities

This paper extends the theory of tradable bottleneck permits system to cases with multiple period markets and designs its implementation mechanism. The multiple period markets can achieve more efficient resource allocation than a single period market when users’ valuations of tradable permits change over time. To implement the multiple period trading markets, we propose an evolutionary mechanism that combines a dynamic auction with a capacity control rule that adjusts a number of permits issued for each market. Then, we prove that the proposed mechanism has the following desirable properties: (i) the dynamic auction is strategy-proof within each period and guarantees that the market choice of each user is optimal under a perfect information assumption of users and (ii) the mechanism maximizes the social surplus in a finite number of iterations. Finally, we show that the proposed mechanism may work well even for an incomplete information case.     

A solution algorithm for a dynamic deterministic user equilibrium assignment model with departure time choice

Abstract

A route-based combined model of dynamic deterministic route and departure time choice and a solution method for many origin and destination pairs is proposed. The divided linear travel time model is used to calculate the link travel time and to describe the propagation of flow over time. For the calculation of route travel times, the predictive ideal route travel time concept is adopted. Solving the combined model of dynamic deterministic route and departure time choice is shown to be equivalent to solving simultaneously a system of non-linear equations. A Newton-type iterative scheme is proposed to solve this problem. The performance of the proposed solution method is demonstrated using a version of the Sioux Falls network. This shows that the proposed solution method produces good equilibrium solutions with reasonable computational cost.     

Tradable credit schemes on networks with mixed equilibrium behaviors

This paper analyzes and designs tradable credit schemes on networks with two types of players, namely, a finite number of Cournot–Nash (CN) players and an infinite number of (infinitesimal) Wardrop-equilibrium (WE) players. We first show that there are nonnegative anonymous credit schemes that yield system optimum, when transaction costs are not considered. We then analyze how transaction costs would affect the trading and route-choice behaviors of both CN and WE players, and discuss the equilibrium conditions on the coupled credit market and transportation network in the presence of transaction costs. A variational inequality is formulated to describe the equilibrium and is subsequently applied to a numerical example to assess the impacts of transaction costs on a tradable credit system. As expected, transaction costs reduce the trading volume of credits and change their market price. They also change the way how players respond to credit charges in their route choices and cause efficiency losses to the credit schemes that are previously designed without considering transaction costs. With transaction costs, travel costs of WE players will likely increase while those of CN players may decrease due to their higher adaptability in routing strategies.     

Effects of task complexity and time pressure on activity-travel choices: heteroscedastic logit model and activity-travel simulator experiment

This paper derives, estimates and applies a discrete choice model of activity-travel behaviour that accommodates potential effects of task complexity and time pressure on decision-making. To the best of our knowledge, this is the first time that both factors (task complexity and time pressure) are jointly captured in a discrete choice model. More specifically, our heteroscedastic logit model captures potential impacts of task complexity and time pressure through the scale of the utility of activity-travel options. We collect data using a novel activity-travel simulator experiment that has been specifically designed with the aim of testing our model. Results are in line with expectations, in that higher levels of task complexity and time pressure are found to result in a smaller scale of utility. In other words, higher levels of task complexity and time pressure lead to more random choice behaviour and as a consequence to less pronounced differences in choice probabilities between alternatives. An empirical illustration suggests that choice probability-differences between models that do and those that do not capture these effects, can be very substantial; this in turn suggests that failing to capture the effects of task complexity and time pressure in discrete choice models of activity travel decision-making might lead to serious bias in forecasts of the effects of transport policies.     

Personal security as a transport issue: a state‐of‐the‐art review

The possibility of experiencing attack, assault, violence, robbery, harassment or other anti‐social behaviour has become an important influence on traveller decision‐making. Fear and apprehension about personal security can affect all aspects of travel choice (e.g. choice of route, mode, time of journey, etc.), including whether to travel at all. Indeed, in some locations an informal curfew is reported to operate. However, both the degree and widespread extent of such fears indicate that unacceptable constraints on personal liberty do exist in many locations.

This paper firstly reviews the extent and nature of the problem considering recorded crime statistics, reported levels of fear and apprehension when using transport facilities and subsequent modifications to travel behaviour. Secondly, a range of possible responses by transport operators, policing agencies and national and local government are examined under four broad headings: policing and staffing; situational crime prevention; social crime prevention; and the level and quality of transport service provision.

It is concluded that personal security considerations need to be explicitly incorporated into decisions concerning the design, planning, operation and management of transport systems. It is also important that institutional structures exist to permit the issue both to be recognized and tackled.     

A novel permit scheme for managing parking competition and bottleneck congestion

Morning commuters may have to depart from home earlier to secure a parking space when parking supply in the city center is insufficient. Recent studies show that parking reservations can reduce highway congestion and deadweight loss of parking competition simultaneously. This study develops a novel tradable parking permit scheme to realize or implement parking reservations when commuters are either homogeneous or heterogeneous in their values of time. It is found that an expirable parking permit scheme with an infinite number of steps, i.e., the ideal-scheme, is superior to a time-varying pricing scheme in the sense that designing a permit scheme does not require commuters’ value of time information and the performance of the scheme is robust to the variation of commuters’ value of time. Although it is impractical to implement the ideal-scheme with an infinite number of steps, the efficiency loss of a permit scheme with finite steps can be bounded in both cases of homogeneous and heterogeneous commuters. Moreover, considering the permit scheme may lead to an undesirable benefit distribution among commuters, we propose an equal cost-reduction distribution of parking permits where auto commuters with higher value of time will receive fewer permits.     

An analysis of instability in a departure time choice problem

This study performs a theoretical analysis of instability in a departure time choice problem. Stability of equilibrium is an important factor for reliability of travel time. If equilibrium is not stable, travel time changes over a period of days even if demand and network performance are stable. This study examines the stability of a dynamic user equilibrium problem by using the departure time choice problem. The mechanism of day‐to‐day changes in a traveller's behaviour is determined first, and then a function that indicates dissimilarity to equilibrium is defined. The day‐to‐day changes in the dissimilarity function are mathematically examined using approximations. A numerical test is also carried out to verify the result. Results of these analyses show that there can be a case where the system does not converge to equilibrium. It is also indicated that this instability should be caused by the non‐monotonicity of the schedule cost.     

Differences in probability weighting for individual travelers: a managed lane choice application

Using a 2012 stated preference survey based on a traveler’s most recent actual trip, this study predicts traveler choices between general purpose lanes and managed lanes for a freeway in Houston, Texas. The choice model incorporates probability weighting for risky travel times. The results indicate significant improvement in predicative power over a model that excludes weighting, confirming non-linearity in the probability weighting function. The maximum value of time (VOT) measures calculated in this study are lower than estimated in many previous route choice studies. This highlights the importance of incorporating individual weights for travel risks. Travelers’ underweighting of travel time risks would help explain the lower VOTs found in our study because respondents consider route choice decision-making as a gamble, but assign their own probabilities of occurrence to arriving at their destination on time, late, or early. We find that traveler groups are heterogeneous and the different weights developed for different groups of travelers can be used to better understand their probabilities. Segmentation analysis indicates that Age may serve to proxy the effects of more experience over time, or changing driving abilities, or changes in one’s sense of optimism or pessimism at different ages. Gender and Income also play a role in how the objective probabilities presented to respondents were translated into subjective probabilities.     

An integrated framework to jointly model decisions of activity time allocation and work-related travel

Five activity-travel choice dimensions, including three activity time allocation decisions and two work-related travel choices, are jointly modeled using the structural equation model in order to accommodate the complex interactions among them. Via a two-step estimation approach, the behavioral pattern underlying activity-travel decisions is explicitly revealed. For example, it demonstrates the priority with respect to subsistence activity, maintenance activity, and recreation activity due to a limited time budget; and bus commuting behavior positively influences the time allocated to the maintenance activity. In addition, two attitudinal factors are constructed and confirmed to have important effects on the five behavioral dimensions, which contribute to reveal the decision-making process from the perspective of psychology. This comprehensive framework is expected to provide important implications for mobility management and urban planning.     

A dynamic route choice model for public transport networks with boarding queues

Abstract

The concepts of optimal strategy and hyperpath were born within the framework of static frequency-based public transport assignment, where it is assumed that travel times and frequencies do not change over time and no overcrowding occurs. However, the formation of queues at public transport stops can prevent passengers from boarding the first vehicle approaching and can thus lead to additional delays in their trip. Assuming that passengers know from previous experience that for certain stops/lines they will have to wait for the arrival of the 2nd, 3rd, …, k-th vehicle, they may alter their route choices, thus resulting in a different assignment of flows across the network. The aim of this paper is to investigate route choice behaviour changes as a result of the formation and dispersion of queues at stops within the framework of optimal travel strategies. A new model is developed, based on modifications of existing algorithms.     

Space,money, life-stage,and the allocation of time

Demographic, socioeconomic, seasonal, and scheduling factors affect the allocation of time to various activities. This paper examines those variables through exploration of the 1990 Nationwide Personal Transportation Survey, which has been inverted to track activity duration. Two key issues are considered. First, how much can activity duration and frequency explain travel duration? The analysis shows activity duration has positive and significant effects on travel duration, supporting recent arguments in favor of activity based models. Second, which recent trend is the main culprit in the rise in travel: suburbanization, rising personal incomes, or female labor force participation? This paper examines the share of time within a 24-hour budget allocated to several primary activities: home, work, shop, and other. The data suggest that income and location have modest effects on time allocation compared with the loss of discretionary time due to working.

     

Perception bias in route choice

Travel time is probably one of the most studied attributes in route choice. Recently, perception of travel time received more attention as several studies have shown its importance in explaining route choice behavior. In particular, travel time estimates by travelers appear to be biased against non-chosen options even if these are faster. In this paper, we study travel time perception and route choice of routes with different degrees of road hierarchy and directness. In the Dutch city of Enschede, respondents were asked to choose a route and provide their estimated travel times for both the preferred and alternative routes. These travel times were then compared with actual travel times. Results from previous studies were confirmed and expanded. The shortest time route was chosen in 41 % of the cases while the perceived shortest time route was chosen by almost 80 % of the respondents. Respondents overestimated travel time in general but overestimated the travel time of non-chosen routes more than the travel time of chosen routes. Perception of travel time depends on road hierarchy and route directness, as more direct routes and routes higher up in the hierarchy were perceived as being relatively fast. In addition, there is evidence that these attributes also influence route choice independently of perceived travel time. Finally, travel time perceptions appear to be most strongly biased against non-chosen options when respondents were familiar with the route or indicated a clear preference for the chosen routes. This result indicates that behavior will be more difficult to change for the regular travelers.     

Investigating the effect of travel time variability on drivers' route choice decisions in Shanghai,China

Abstract

This paper investigates the effect of travel time variability on drivers' route choice behavior in the context of Shanghai, China. A stated preference survey is conducted to collect drivers' hypothetical choice between two alternative routes with designated unequal travel time and travel time variability. A binary choice model is developed to quantify trade-offs between travel time and travel time variability across various types of drivers. In the model, travel time and travel time variability are, respectively, measured by expectation and standard deviation of random travel time. The model shows that travel time and travel time variability on a route exert similarly negative effects on drivers' route choice behavior. In particular, it is found that middle-age drivers are more sensitive to travel time variability and less likely to choose a route with travel time uncertainty than younger and elder drivers. In addition, it is shown that taxi drivers are more sensitive to travel time and more inclined to choose a route with less travel time. Drivers with rich driving experience are less likely to choose a route with travel time uncertainty.     

Analysis of long-distance vacation travel demand in the United States: a multiple discrete–continuous choice framework

This study analyzes the annual vacation destination choices and related time allocation patterns of American households. More specifically, an annual vacation destination choice and time allocation model is formulated to simultaneously predict the different vacation destinations that a household visits in a year, and the time (no. of days) it allocates to each of the visited destinations. The model takes the form of a multiple discrete–continuous extreme value (MDCEV) structure. Further, a variant of the MDCEV model is proposed to reduce the prediction of unrealistically small amounts of vacation time allocation to the chosen destinations. To do so, the continuously non-linear utility functional form in the MDCEV framework is replaced with a combination of a linear and non-linear form. The empirical analysis was performed using the 1995 American Travel Survey data, with the United States divided into 210 alternative destinations. The model estimation results provide several insights into the determinants of households’ vacation destination choice and time allocation patterns. Results suggest that travel times and travel costs to the destinations, and lodging costs, leisure activity opportunities (measured by employment in the leisure industry), length of coastline, and weather conditions at the destinations influence households’ destination choices for vacations. The annual vacation destination choice model developed in this study can be incorporated into a larger national travel modeling framework for predicting the national-level, origin–destination flows for vacation travel.     

Forecasting high-speed rail ridership using a simultaneous modeling approach

Abstract

The newly launched, June 2009, US High-Speed Intercity Passenger Rail Program has rekindled a renewed interest in forecasting high-speed rail (HSR) ridership. The first step to the concerted effort by the federal, state, rail, and other related agencies to develop a nationwide HSR network is the development of credible approaches to forecast the ridership. This article presents a nested logit/simultaneous choice model to improve the demand forecast in the context of intercity travel. In addition to incorporating the interrelationship between trip generation and mode choice decisions, the simultaneous model also provides a platform for the same utility function flowing between both the decision-making processes. Using American Travel Survey data, supplemented by various mode parameters, the proposed model improves the forecast accuracy and confirms the significant impact of travel costs on both mode choice and trip generation. Furthermore, the cross elasticity of mode choice and trip generation related to travel costs and other modal characteristics may shed some light on transportation policies in the area of intercity travel, especially in anticipation of HSR development.     

Tradable credits scheme and transit investment optimization for a two‐mode traffic network

This paper proposes an optimization model to minimize the “system costs” and guide travelers' behavior by exploring the optimal bus investment and tradable credits scheme design in a bimodal transportation system. Travelers' transport mode choice behavior (car or bus) and the modal equilibrium conditions between these two forms of transport are studied in the tradable credits scheme. Public transport priority is highlighted by charging car travelers credits only. The economies of scale presented by the transit system under the tradable credit scheme are analyzed by comparing the marginal cost and average cost. Numerical examples are presented to demonstrate the model. Furthermore, the effects of tradable credits schemes on bus investment and travelers' modal choice behavior are explored based on scenario discussions. Copyright © 2016 John Wiley & Sons, Ltd.     

Measuring and enhancing the transferability of hidden Markov models for dynamic travel behavioral analysis

As an emerging dynamic modeling method that incorporates time-dependent heterogeneity, hidden Markov models (HMM) are receiving increased research attention with regards to travel behavior modeling and travel demand forecasting. This paper focuses on the model transferability of HMM. Based on a series of transferability and goodness-of-fit measures, it finds that HMMs have a superior performance in predicting future transportation mode choice, compared to conventional choice models. Aimed at further enhancing its transferability, this paper proposes a Bayesian conditional recalibration approach that maps the model prediction directly to the context data. Compared to traditional model transferring methods, the proposed approach does not assume fixed parameterization and recalibrates the utilities and the prediction directly. A comparison between the proposed approach and the traditional transfer-scaling favors our approach, with higher goodness-of-fit. This paper fills the gap in understanding the transferability of HMM and proposes a practical method that enables potential applications of HMM.