Estimation of value of travel time reliability

In mode choice decision, travelers consider not only travel time but also reliability of its modes. In this paper, reliability was expressed in terms of standard deviation and maximum delay that were measured based on triangular distribution. In order to estimate value of time and value of reliability, the Multinomial and Nested Logit models were used. The analysis results revealed that reliability is an important factor affecting mode choice decisions. Elasticity is used to estimate the impacts of the different policies and system improvements for water transportation mode. Among these policies, decision maker can assess and select the best alternative by doing the benefit and cost analysis based on a new market share, the value of time, and the value of reliability. Finally, a set of promising policies and system improvement of the water transportation were proposed.     

Tracking daily travel; Assessing discrepancies between GPS-derived and self-reported travel patterns

Global Positioning Systems (GPS) technologies have been used in conjunction with traditional one- or two-day travel diaries to audit respondent reporting patterns, but we used GPS-based monitoring to conduct the first assessment to our knowledge of travel reporting patterns using a seven-day travel log instrument, which could reduce response burden and provide multiple-day, policy-relevant information for evaluation studies. We found substantial agreement between participant-reported daily travel patterns and GPS-derived patterns among 116 adult residents of a largely low-income and non-white transportation corridor in urbanized Los Angeles in 2011–2013. For all modes, the average difference between daily GPS- and log-derived trip counts was only about 0.39 trips and the average difference between daily GPS- and log-derived walking duration was about −11.8 min. We found that the probability that a day would be associated with agreement or discrepancies between these measurement tools varied by travel mode and participant socio-demographic characteristics. Future research is needed to investigate the potential and limitations of this and other self-report instruments for a larger sample and a wider range of population groups and travel patterns.     

A micro-analysis of land use and travel in five neighborhoods in the San Francisco Bay Area

This study examined the effects of land use and attitudinal characteristics on travel behavior for five diverse San Francisco Bay Area neighborhoods. First, socio-economic and neighborhood characteristics were regressed against number and proportion of trips by various modes. The best models for each measure of travel behavior confirmed that neighborhood characteristics add significant explanatory power when socio-economic differences are controlled for. Specifically, measures of residential density, public transit accessibility, mixed land use, and the presence of sidewalks are significantly associated with trip generation by mode and modal split. Second, 39 attitude statements relating to urban life were factor analyzed into eight factors: pro-environment, pro-transit, suburbanite, automotive mobility, time pressure, urban villager, TCM, and workaholic. Scores on these factors were introduced into the six best models discussed above. The relative contributions of the socio-economic, neighborhood, and attitudinal blocks of variables were assessed. While each block of variables offers some significant explanatory power to the models, the attitudinal variables explained the highest proportion of the variation in the data. The finding that attitudes are more strongly associated with travel than are land use characteristics suggests that land use policies promoting higher densities and mixtures may not alter travel demand materially unless residents' attitudes are also changed.     

Multimodal travel groups and attitudes: A latent class cluster analysis of Dutch travelers

For developing sustainable travel policies, it may be helpful to identify multimodal travelers, that is, travelers who make use of more than one mode of transport within a given period of time. Of special interest is identifying car drivers who also use public transport and/or bicycle, as this group is more likely to respond to policies that stimulate the use of those modes. It is suggested in the literature that this group may have less biased perceptions and different attitudes towards those modes. This supposition is examined in this paper by conducting a latent class cluster analysis, which identifies (multi)modal travel groups based on the self-reported frequency of mode use. Simultaneously, a membership function is estimated to predict the probability of belonging to each of the five identified (multi)modal travel groups, as a function of attitudinal variables in addition to structural variables. The results indicate that the (near) solo car drivers indeed have more negative attitudes towards public transport and bicycle, while frequent car drivers who also use public transport have less negative public transport attitudes. Although the results suggest that in four of the five identified travel groups, attitudes are congruent with travel mode use, this is not the case for the group who uses public transport most often. This group has relatively favorable car attitudes, and given that many young, low-income travelers belong to this group, it may be expected that at least part of this group will start using car more often once they can afford it. Based on the results, challenges for sustainable policies are formulated for each of the identified (multi)modal travel groups.     

Gauging interventions for sustainable travel: A comparative study of travel attitudes in Berlin and London

So-called ‘soft’ policy instruments that respond to the psychological aspects of travel are regularly acknowledged as necessary complements to ‘hard’ infrastructure investments to effectively promote sustainable travel in cities. While studies investigating subjective orientations among travellers have proliferated, open questions remain including the role of recent technological advances, the expansion of alternative mobility services, locally specific mobility cultures and residential selection. This paper presents the methods, results and policy implications of a comparative study aiming to understand mobility attitudes and behaviours in the wider metropolitan regions of Berlin and London. We specifically considered information and communication technology (ICT), new types of mobility services such as car sharing, electric cars and residential preferences. In each region, we identified six comparable segments with distinct attitudinal profiles, socio-demographic properties and behavioural patterns. Geocoding of the home address of respondents further revealed varying contextual opportunities and constraints that are likely to influence travel attitudes. We find that there is significant potential for uptake of sustainable travel practices in both metropolitan regions, if policy interventions are designed and targeted in accordance with group-specific needs and preferences and respond to local conditions of mobility culture. We identify such interventions for each segment and region and conclude that comparative assessment of attitudinal, alongside geographical, characteristics of metropolitan travellers can provide better strategic input for realistic scenario-building and ex-ante assessment of sustainable transport policy.     

Estimation of the value of travel time and of travel time reliability for heterogeneous drivers in a road network

Transportation - Travel time reliability has been recognized as an important factor in cost–benefit analysis in a transportation network. To estimate the benefit and cost of travel time...     

Attitudes to transport modes for shopping purposes in Singapore

A review of the retailing and transportation literature shows that there is an apparently large research gap in the understanding of shoppers’ attitudes towards transport modes for shopping purposes. In particular, not many studies have been done to examine shoppers’ detailed and disaggregated attitudinal image structures of the various transport modes for shopping purposes. Using Singapore as a study area, this paper has attempted to investigate shoppers’ image structure of transport modes and their comparative perception ratings for the various modes of transportation for shopping purposes. Using principal component analysis, the research has unveiled the perceptions of five transport modes for shopping purposes. While some of the factors are unique to certain transport modes, other factors, namely ‘suitability’, ‘practicality’, ‘ease of travel’ and ‘cost’, are common to all modes of transportation. By way of the weighted‐factor rating, the study found that the car recorded the highest overall perception rating while the bus rated the lowest. In addition, the shoppers have reasonably good perception of public transport modes in Singapore.     

Identifying travel mode with GPS data

Travel mode identification is an essential step in travel information detection with global positioning system (GPS) survey data. This paper presents a hybrid procedure for mode identification using large-scale GPS survey data collected in Beijing in 2010. In a first step, subway trips were detected by applying a GPS/geographic information system (GIS) algorithm and a multinomial logit model. A comparison of the identification results reveals that the GPS/GIS method provides higher accuracy. Then, the modes of walking, bicycle, car and bus were determined using a nested logit model. The combined success rate of the hybrid procedure was 86%. These findings can be used to identify travel modes based on GPS survey data, which will significantly improve the efficiency and accuracy of travel surveys and data analysis. By providing crucial travel information, the results also contribute to modeling and analyzing travel behaviors and are readily applicable to a wide range of transportation practices.     

A green intermodal service network design problem with travel time uncertainty

In a more and more competitive and global world, freight transports have to overcome increasingly long distances while at the same time becoming more reliable. In addition, a raising awareness of the need for environmentally friendly solutions increases the importance of transportation modes other than road. Intermodal transportation, in that regard, allows for the combination of different modes in order to exploit their individual advantages. Intermodal transportation networks offer flexible, robust and environmentally friendly alternatives to transport high volumes of goods over long distances. In order to reflect these advantages, it is the challenge to develop models which both represent multiple modes and their characteristics (e.g., fixed-time schedules and routes) as well as the transhipment between these transportation modes. In this paper, we introduce a Green Intermodal Service Network Design Problem with Travel Time Uncertainty (GISND-TTU) for combined offline intermodal routing decisions of multiple commodities. The proposed stochastic approach allows for the generation of robust transportation plans according to different objectives (i.e., cost, time and greenhouse gas (GHG) emissions) by considering uncertainties in travel times as well as demands with the help of the sample average approximation method. The proposed methodology is applied to a real-world network, which shows the advantages of stochasticity in achieving robust transportation plans.     

Reducing average comprehensive travel cost by rationally allocating trips to different travel modes

To study the effect of different transport policies on reducing the average comprehensive travel cost (CTC) of all travel modes, by increasing public transport modal share and decreasing car trips, an optimization model is developed based on travel cost utility. A nested logit model is applied to analyze trip modal split. A Genetic Algorithm is then used to determine the implementation of optimal solutions in which various transport policies are applied in order to reduce average CTC. The central urban region of Beijing is selected as the study area in this research. Different policies are analyzed for comparison, focusing on their optimal impacts on minimizing the average CTC utility of all travel modes by rationally allocating trips to different travel modes in the study area. It is found that the proposed optimization model provides a reasonable indication of the effect of policies applied.     

The socio-economics of travel behavior and environmental burdens: A Detroit,Michigan regional context

The transportation industry—particularly light-duty vehicles—is a significant contributor of greenhouse gasses, accounting for about one-third of overall emissions in the U.S. Research to date has studied various factors that impact travel behavior of residents with varying socio-economic characteristics. However, research on the socio-economic characteristics of residents and their impact on environmental burdens within a single urban region, as measured by fuel consumption and vehicular emissions, is recognized as under-represented in the U.S. planning and transportation literature. This study focuses on the Detroit region, Michigan, a unique case study due to the scale of suburbanization and urban decline, yet representative of many mid-western cities. The article explores how socio-economic characteristics impact travel patterns and environmental burdens within six Detroit region neighborhoods. Data on individual travel behavior and personal vehicle characteristics gathered from a mail survey enabled an analysis into how associated environmental burdens varied with socio-economic composition. The analysis explores contributions to environmental burdens between poorer urban and wealthier suburban populations.     

Finding the most reliable path with and without link travel time correlation: A Lagrangian substitution based approach

Path travel time reliability is an essential measure of the quality of service for transportation systems and an important attribute in travelers’ route and departure time scheduling. This paper investigates a fundamental problem of finding the most reliable path under different spatial correlation assumptions, where the path travel time variability is represented by its standard deviation. To handle the non-linear and non-additive cost functions introduced by the quadratic forms of the standard deviation term, a Lagrangian substitution approach is adopted to estimate the lower bound of the most reliable path solution through solving a sequence of standard shortest path problems. A subgradient algorithm is used to iteratively improve the solution quality by reducing the optimality gap. To characterize the link travel time correlation structure associated with the end-to-end trip time reliability measure, this research develops a sampling-based method to dynamically construct a proxy objective function in terms of travel time observations from multiple days. The proposed algorithms are evaluated under a large-scale Bay Area, California network with real-world measurements.     

Practical approach for travel time estimation from point traffic detector data

Accurate estimation of travel time is critical to the success of advanced traffic management systems and advanced traveler information systems. Travel time estimation also provides basic data support for travel time reliability research, which is being recognized as an important performance measure of the transportation system. This paper investigates a number of methods to address the three major issues associated with travel time estimation from point traffic detector data: data filling for missing or error data, speed transformation from time‐mean speed to space‐mean speed, and travel time estimation that converts the speeds recorded at detector locations to travel time along the highway segment. The case study results show that the spatial and temporal interpolation of missing data and the transformation to space‐mean speed improve the accuracy of the estimates of travel time. The results also indicate that the piecewise constant‐acceleration‐based method developed in this study and the average speed method produce better results than the other three methods proposed in previous studies. Copyright © 2011 John Wiley & Sons, Ltd.     

Validation of a multimodal travel simulator with travel information provision

This paper presents a computer based travel simulator for collecting data concerning the use of next-generation ATIS and their effects on traveler decision making in a multimodal travel environment. The tool distinguishes itself by presenting a completely abstract multimodal transport network, where knowledge levels are fully controlled for in terms of awareness of mode-route combinations as well as in terms of variability of characteristics of known alternatives. Furthermore, it contains an information service-module that provides a variety of advanced types of travel information, with controlled for levels of reliability. These novel features warrant an extensive validation of the travel simulator. Such a validation is performed, using two datasets (one with revealed data, one with data from an experiment held in the simulator) obtained among 264 participants. Results suggest that the simulator succeeds in collecting useful data on multimodal travel choice making under provision of advanced types of travel information.     

Structural equation modeling for national travel demand: an examination of the relationships between communication modes

This paper examines the relationships among different transportation modes, and between transportation and telecommunications, by applying the structural equation modeling (SEM) technique. For this purpose, we collected and compiled time series data on national travel demand, and socioeconomic and telecommunications conditions in Taiwan, and built national travel demand models using SEM. The estimation results show that the relationship between telecommunications and transportation demand (either car ownership or public transportation) is more complementary than substitutional. Moreover, car ownership is a type of inelastic necessity good, and its relationship with public transportation is more substitutional than complementary. Finally, among the three public transportation modes – rail, bus and domestic air – it is found that air is weakest in terms of competitive power. From the viewpoint of long-term forecasting trends, the bus holds its competitive power in comparison with other public transportation modes and would not be replaced in the future.     

Household travel attributes transferability analysis: application of a hierarchical rule based approach

This paper presents a system of hierarchical rule-based models of trip generation and modal split. Travel attributes, like trip counts for different transportation modes and commute distance, are among the modeled variables. The proposed framework could be considered as an alternative for several modules of the traditional travel demand modeling approach, while providing travel attributes at the highly disaggregate level that can be also used in activity-based micro-simulation modeling systems. Nonetheless, the modeling framework of this study is not considered as a substitute for activity-based models. The explanatory variables set ranges from socio-economic and demographic attributes of the household to the built environment characteristics of the household residential location. Another important contribution of the study is a framework in which travel attributes are modeled in conjunction with each other and the interdependencies among them are postulated through a hierarchical system of models. All the models are developed using rule-based decision tree method. Moreover, the models developed in this study present a useful improvement in increasing the practicality and accuracy of the rule-based travel data simulation models.     

Comparison on travel scheduling between driving and walking trips by habitual car users

Research on walking behavior has become increasingly more important in the field of transportation in the past decades. However, the study of the factors influencing the scheduling decisions related to walking trips and the exploration of the differences between travel modes has not been conducted yet. This paper presents a comparison of the scheduling and rescheduling decisions associated with car driving trips and walking trips by habitual car users using a data set collected in Valencia (Spain) in 2010. Bivariate probit models with sample selection are used to accommodate the influence of pre-planning on the decision to execute a travel as pre-planned or not. The explicative variables considered are: socio-economic characteristics of respondents, travel characteristics, and facets of the activity executed at origin and at destination including the scheduling decisions associated with them. The results demonstrate that a significant correlation exists between the choices of pre-planning and rescheduling for both types of trips. Whether for car driving or walking trips, the scheduling decisions associated with the activity at origin and at destination are the most important explicative factors of the trip scheduling and rescheduling decisions. However, the rescheduling of trips is mainly influenced by modifications in the activity at destination. Some interesting differences arise regarding the rescheduling decision processes between travel modes: if pre-planned, walking trips are less likely to be modified than car driving trips, showing a more rigid rescheduling behavior.     

Long-term trends in domestic US passenger travel: the past 110 years and the next 90

Based upon a long-term historical data set of US passenger travel, a model is estimated to project aggregate transportation trends through 2100. One of the two model components projects total mobility (passenger-km traveled) per capita based on per person GDP and the expected utility of travel mode choices (logsum). The second model component has the functional form of a logit model, which assigns the projected travel demand to competing transportation modes. An iterative procedure ensures the average amount of travel time per person to remain at a pre-specified level through modifying the estimated value of time. The outputs from this model can be used as a first-order estimate of a future benchmark against which the effectiveness of various transportation policy measures or the impact of autonomous behavioral change can be assessed.     

Characteristics analysis for travel behavior of transportation hub passengers using mobile phone data

The travel behavior of passengers from the transportation hub within the city area is critical for travel demand analysis, security monitoring, and supporting traffic facilities designing. However, the traditional methods used to study the travel behavior of the passengers inside the city are time and labor consuming. The records of the cellular communication provide a potential huge data source for this study to follow the movement of passengers. This study focuses on the passengers’ travel behavior of the Hongqiao transportation hub in Shanghai, China, utilizing the mobile phone data. First, a systematic and novel method is presented to extract the trip information from the mobile phone data. Several key travel characteristics of passengers, including passengers traveling inside the city and between cities, are analyzed and compared. The results show that the proposed method is effective to obtain the travel trajectories of mobile phone users. Besides, the travel behavior of incity passengers and external passengers are quite different. Then, the correlation analysis of the passengers’ travel trajectories is provided to research the availability of the comprehensive area. Moreover, the results of the correlation analysis further indicate that the comprehensive area of the Hongqiao hub plays a relatively important role in passengers’ daily travel.