Service effectiveness of prt vs collective ‐ corridor transport

A generalised model is used to provide estimates of overall trip times and speed for conventional corridor‐collective transport and PRT. The results demonstrate why traditional forms of transport find difficulty providing an effective service in a city. Short separations between stops are required to minimise walk times but on conventional transport this leads to significant reductions in achievable speed because of the need for frequent stops. It is also shown that there is very little benefit in service effectiveness from LRT/APM/Monorail over buses. PRT is immune to these effects. The present calculations typically show a benefit for PRT of a factor of two or greater in trip time over either bus or LRT/APM.     

An intersection control strategy for a short‐headway P.R.T. network

Much PRT development and research is currently being undertaken assuming quasi‐synchronous longitudinal control of guideway vehicles. This method of control has the characteristic that intersection performance has a substantial influence on the efficiency of trip demand processing. An algorithm for the control of a PRT intersection is discussed here, which would appear to have significant advantages over all other known existing stratagems. The stratagem is not only efficient but its flexibility facilitates tailoring to diverse local conditions; furthermore, the algorithm does not require intractable computations or excessive computer memory requirements. The algorithm is described and simulation results are presented. A comparative study is also made between this algorithm and its fore‐runner.     

The geographical stability of a typical trip production model: Applications of national and local data in four urban areas

Estimates of the numbers of trips likely to be made by individuals and of the modes of transport that will be available to them for those trips are provided by the trip production model. The objective of the work described in this paper was to investigate the geographical stability of the trip production model by comparing the numbers of trips estimated by the model when using national rather than local data. The 1972/3 National Travel Survey was used as the national data. Household interview survey data from the transportation studies of Lincoln, Sheffield/Rotherham, South East Dorset and Bristol were the local data sources. Three home based trip purposes are modelled; 24 hour work, 24 hour shop, 24 hour other.The models calibrated from national and local data perform similarly provided both operate with local trip rates. The car ownership sub-model with national parameters produces similar forecasts to the models with local parameters. There are probably real differences in household trip rates for some trip purposes between urban areas.     

Running for the 7:45: The effects of public transit improvements on commuter stress

This research project took advantage of the implementation of a major mass transit improvement by New Jersey Transit which provided a "one-seat ride" into New York City for many commuters who previously had to transfer in Hoboken in order to take Port Authority Trans Hudson (PATH) trains into New York City. The creation of this new service provided a natural experiment in which some riders switched to the new route, while others continued to use their previous route. We studied psychological and psychophysiological responses to these commuting options, using a quasi-experimental, pre-post change, field research design.We found that riders on this new line had lower levels of stress, as multiply measured, than they had earlier, before the advent of this new train, or than did other riders currently using the Hoboken-PATH option. The stress effects seemed to be mediated by the time of the trip – that is, the reduced trip time of the new, direct service seemed to be a primary factor in the reduced stress to riders. Predictability of the trip was also inversely correlated with stress, but did not distinguish between the commuter groups. These results were largely replicated with a student group who rode the same lines acting as simulated commuters.     

The effect of non-recreational transport cycling on use of other transport modes: A cross-sectional on-line survey

Accurate modelling of the health and environmental benefits of non-recreational transport cycling requires information about its effects on the use of other transport modes. Relevant research has not focussed on cycling for transport in a general context (as opposed to bikeshare), nor allowed for multi-modal trips. The influence of trip- and personal-characteristics on whether cycling replaces car-driving have yet to be considered. The present study aimed to address these research gaps. An on-line cross-sectional survey was completed by 1525 Australians who cycle for transport at least once per week. For the most recent trip completed (at least partly) by bicycle participants provided trip distance, and percentage travelled by car, public transport (PT), and walking. They also provided the percentage travelled by each mode for the same trip before taking up transport cycling; and a hypothetical future trip when riding is not possible. Compared to the same trip before, fifty percent of recent trips reduced car use, and around 1/3 eliminated a 100%-car trip. Reduced car use was significantly less likely for trips under 7.5 km, commuting, females, respondents under 55, and regular cyclists. Reduced car use was less likely for respondents who started riding because it is flexible, and more likely for those who started riding to avoid parking. Car-use was reduced by an average of 6.2 km per trip, and each bicycle-km cycled replaced 0.5 car-km. Participants report that since taking up cycling, even when they cannot use their bike, they use cars less and use PT more compared to before they took up cycling. Results suggest that previous studies underestimated the extent to which transport cycling replaces car travel, and highlights trip types and population groups to target with cycling promotion strategies. Information about the per-trip and per-bicycle-km replacement of car, PT and walking may be used for more accurate estimation of the benefits of transport cycling than has hitherto been possible.     

Can rural older drivers meet their needs without a car? Stated adaptation responses from a GPS travel diary survey

Rural seniors are highly dependent on their automobile to meet their trip making needs, yet the effects of aging can make access to the vehicle difficult or impossible over time. The anticipated growth in the older person population, in concert with limited travel data available to support rural transportation planning in Canada suggests a disconnect between what rural older people may require for transportation and the availability of formal alternatives. Many will seek informal alternatives to driving, such as depending on friends and family, to meet their travel needs, but the degree is not well understood in the context of their actual vehicle usage and stated ability to adapt. This paper draws from a Global Positioning System (GPS)-based multi-day travel diary survey of a convenience sample of 60 rural older drivers (29 men, 31 women, average age of 69.6 years) from New Brunswick, Canada. Participants would rely on “friends and family” for 52% of all trips they undertook as driver in the survey, “walk or bike” for 14% of trips, and “not take the trip” in 34% of trips if they did not have access to a vehicle. The formal option of “Transit” was not selected as a viable alternative by any participant for any trip. “Medical trips”, “Shopping” and “Personal Errands” were the least discretionary of all trip types, yet the most difficult for participants to find alternate arrangements. This suggests the need to explore different models of service delivery, such as a community-supported, member-based rural shuttle service with volunteer and paid drivers that build on informal social networks and can provide service when friends and family are unavailable.     

Analysis of vehicle emission measurements on the new WLTC,the NEDC and the CADC

Several studies have shown that the type-approval data is not representative for real-world usage. Consequently, the emissions and fuel consumption of the vehicles are underestimated. Aiming at a more dynamic and worldwide harmonised test cycle, the new Worldwide Light-duty Test Cycle is being developed. To analyse the new cycle, we have studied emission results of a test programme of six vehicles on the test cycles WLTC (Worldwide Light-duty Test Cycle), NEDC (New European Driving Cycle) and CADC (Common Artemis Driving Cycle). This paper presents the results of that analysis using two different approaches. The analysis shows that the new driving cycle needs to exhibit realistic warm-up procedures to demonstrate that aftertreatment systems will operate effectively in real service; the first trip of the test cycle could have an important contribution to the total emissions depending on the length of the trip; and that there are some areas in the acceleration vs. vehicle speed map of the new WLTC that are not completely filled, especially between 70 and 110 km/h. For certain vehicles, this has a significant effect on total emissions when comparing this to the CADC.     

Activity imputation for trip-chains elicited from smart-card data using a continuous hidden Markov model

Although smart-card data were expected to substitute for conventional travel surveys, the reality is that only a few automatic fare collection (AFC) systems can recognize an individual passenger's origin, transfer, and destination stops (or stations). The Seoul metropolitan area is equipped with a system wherein a passenger's entire trajectory can be tracked. Despite this great advantage, the use of smart-card data has a critical limitation wherein the purpose behind a trip is unknown. The present study proposed a rigorous methodology to impute the sequence of activities for each trip chain using a continuous hidden Markov model (CHMM), which belongs to the category of unsupervised machine-learning technologies. Coupled with the spatial and temporal information on trip chains from smart-card data, land-use characteristics were used to train a CHMM. Unlike supervised models that have been mobilized to impute the trip purpose to GPS data, A CHMM does not require an extra survey, such as the prompted-recall survey, in order to obtain labeled data for training. The estimated result of the proposed model yielded plausible activity patterns that are intuitively accountable and consistent with observed activity patterns.     

Dynamics of commuting decision behaviour under advanced traveller information systems

This paper addresses departure time and route switching decisions made by commuters in response to Advanced Traveller Information Systems (ATIS). It is based on the data collected from an experiment using a dynamic interactive travel simulator for laboratory studies of user responses under real-time information. The experiment involves actual commuters who simultaneously interact with each other within a simulated traffic corridor that consists of alternative travel facilities with differing characteristics. These commuters can determine their departure time and route at the origin and their path en-route at various decision nodes along their trip. A multinomial probit model framework is used to capture the serial correlation arising from repeated decisions made by the same respondent. The resulting behavioural model estimates support the notion that commuters' route switching decisions are predicated on the expectation of an improvement in trip time that exceeds a certain threshold (indifference band), which varies systematically with the remaining trip time to the destination, subject to a minimum absolute improvement (about 1 min).     

Modeling the modal choice decision process

Most models of modal choice are macroanalytic in nature — focusing on the behavior of large groups of travelers — and have limited explanatory power. Transportation managers need to know more about the decision processes of individual travelers in selecting a mode for a particular trip, if they are to be able to develop strategies for influencing these decisions. A microanalytic model of modal choice is therefore developed in flow-chart form, clarifying the stages in the modal choice decision process for any given trip. Individual consumers are seen as trying to satisfy a particular travel need by first specifying the characteristics of the trip itself and then specifying the “ideal” modal attributes required for this trip. Next, the perceived characteristics of a limited number of modes are evaluated against this “ideal” solution and the consumer is assumed to select that mode which provides the best match. The model explicitly recognizes the impact of psychological variables on modal choice as well as the consumer's need for information if he or she is to evaluate realistically all alternatives.     

Energy impact evaluation for eco-routing and charging of autonomous electric vehicle fleet: Ambient temperature consideration

This paper studies the heterogeneous energy cost and charging demand impact of autonomous electric vehicle (EV) fleet under different ambient temperature. A data-driven method is introduced to formulate a two-dimensional grid stochastic energy consumption model for electric vehicles. The energy consumption model aids in analyzing EV energy cost and describing uncertainties under variable average vehicle trip speed and ambient temperature conditions. An integrated eco-routing and optimal charging decision making framework is designed to improve the capability of autonomous EV’s trip level energy management in a shared fleet. The decision making process helps to find minimum energy cost routes with consideration of charging strategies and travel time requirements. By taking advantage of derived models and technologies, comprehensive case studies are performed on a data-driven simulated transportation network in New York City. Detailed results show us the heterogeneous energy impact and charging demand under different ambient temperature. By giving the same travel demand and charging station information, under the low and high ambient temperature within each month, there exist more than 20% difference of overall energy cost and 60% difference of charging demand. All studies will help to construct sustainable infrastructure for autonomous EV fleet trip level energy management in real world applications.     

Trip chain complexity: a comparison among latent classes of daily mobility patterns

This paper studies the relationship between trip chain complexity and daily travel behaviour of travellers. While trip chain complexity is conventionally investigated between travel modes, our scope is the more aggregated level of a person’s activity-travel pattern. Using data from the Netherlands Mobility Panel, a latent class cluster analysis was performed to group people with similar mode choice behaviour in distinct mobility pattern classes. All trip chains were assigned to both a travel mode and the mobility pattern class of the traveller. Subsequently, differences in trip chain complexity distributions were analysed between travel modes and between mobility pattern classes. Results indicate considerable differences between travel modes, particularly between multimodal and unimodal trip chains, but also between the unimodal travel modes car, bicycle, walking and public transport trip chains. No substantial differences in trip chain complexity were found between mobility pattern classes. Independently of the included travel modes, the distributions of trip chain complexity degrees were similar across mobility pattern classes. This means that personal circumstances such as the number of working hours or household members are not systematically translated into specific mobility patterns.

     

Passengers’ activities during short trips on the London Underground

The present study considers underground passengers and investigates the ways in which they spend their time during a trip of average length or shorter. Using a structured procedure that had been refined after a preliminary study, more than 1,700 passengers were observed in London. The results showed that even when the length of travel is very short (2–6 stops), underground passengers engage in several occupations, especially those involving the use of mobile Information and Communication Technologies. These occupations depend on the specific spatial and temporal conditions of the travel, as well as on gender and age. These results should be useful in designing travel services that enhance passengers’ experiences; they also suggest a criterion for comparing trips using different transportation modes (i.e., looking at the time point during the trip at which the ratio of active versus passive occupations changes).     

Exploring key determinants of travel satisfaction for multi-modal trips by different traveler groups

Using a primary dataset from an experimental survey in eight European cities, this study identified the key determinants of satisfaction with individual trip stages as well as overall journey experience for different travel modes and traveler groups. Multivariate statistical analyses were used to examine the relationships between overall satisfaction and travel experience variables, trip complexity, subjective well-being indices, travel-related attitudes as well as individual- and trip-specific attributes. The results indicate that for certain traveler groups, such as women, young and low-income or unemployed travelers, there are distinctive determinants of satisfaction with trip stages for various travel modes. The results also indicate that satisfaction with the primary trip stage is strongly linked to overall trip satisfaction, while satisfaction levels with access and egress trip stages are strongly related to satisfaction with the primary trip stage. Past experience, traveler expectations and attitudes, and the emotional state of travelers are also significant explanatory variables for travel satisfaction. The results indicate that when an individual consciously chooses a particular travel mode, they will report a higher level of satisfaction with that chosen mode. Notwithstanding, while past experience highly influences an individual’s current travel satisfaction, the more they travel with the current mode, the less satisfied they are with their choice. The results of this study highlight the importance of gaining a better understanding of the interaction between instrumental variables and non-instrumental variables at different trip stages and the influence on user preferences, satisfaction and decision-making processes.     

The utilisation and user characteristics of Uber services in London

ABSTRACT

Ridesourcing services such as Uber are nowadays a common feature within available transport options of many cities around the world (E.g. London & San Francisco). There has been much publicity about the potential impacts of ridesourcing services and how (or if) they should be managed or regulated without an objective understanding of who uses these services and why, as well as its current and future implications for public transport (PT).

Ridesourcing is part of a broader tech-driven, mobile app-based sharing phenomenon – the ‘sharing economy’ – which has disrupted traditional market models and industries, for example, the transport industry, where new players such as Uber have emerged and have quickly become part of the urban transport landscape. Uber has been at the forefront in disrupting the transport sector since its first launch in 2010 (San Francisco, USA). Since its launch, Uber has generated extensive media coverage and debate among policymakers, transport planners and transport authorities on how these services are affecting traditional transport modes such as buses and taxis. However, without objective empirical data – in terms of impacts on trip making characteristics, PT ridership and congestion – policymakers and transport regulators are yet to fully understand the real impacts ridesourcing services are having on the transport network.

This paper is part of broader research that aims to provide insights and empirical-based evidence on how Uber services are used (UberX and Uberpool) in London. A comprehensive survey was undertaken using a detailed questionnaire, issued to UberX and Uberpool users in London to gather detailed data on who uses the Uber services, why they use it and what are the trip purposes, in order to understand Uber user demographics and what effects (if any) Uber services are having on PT usage and trip making characteristics in London. The final findings provide important insights on Uber user demographics, trip purposes, types of trips replaced, impact on car ownership and why travellers use Uber services.     

Intrapersonal variability in daily urban travel behavior: Some additional evidence

Researchers have used multiday travel data sets recently to examine day-to-day variability in travel behavior. This work has shown that there is considerable day-to-day variation in individuals' urban travel behavior in terms of such indicators of behavior as trip frequency, trip chaining, departure time from home, and route choice. These previous studies have also shown that there are a number of important implications of the observed day-to-day variability in travel behavior. For example, it has been shown that it may be possible to improve model parameter estimation precision, without increasing the cost of data collection, by drawing a multiday sample (rather than a single day sample) of traveler behavior, if there is considerable day-to-day variability in the phenomenon being modeled. This paper examines day-to-day variability in urban travel using a three-day travel data set collected recently in Seattle, WA. This research replicates and extends previous work dealing with day-to-day variability in trip-making behavior that was conducted with data collected in Reading, England, in the early 1970s. The present research extends the earlier work by examining day-to-day variations in trip chaining and daily travel time in addition to the variation in trip generation rates. Further, the present paper examines day-to-day variations in travel across the members of two-person households. This paper finds considerable day-to-day variability in the trip frequency, trip chaining and daily travel time of the sample persons and concludes that, in terms of trip frequency, the level of day-to-day variability is very comparable to that observed previously with a data set collected almost 20 years earlier in Reading, England. The paper also finds that day-to-day variability in daily travel time is similar in magnitude to that in daily trip rates. The analysis shows that the level of day-to-day variability is about the same for home-based and non-homebased trips, thus indicating that day-to-day variability in total trip-making is attributable to variation in both home-based and non-home-based trips. Day-to-day variability in the travel behaviors of members of two-person households was also found to be substantial.     

Development of a probabilistic model to optimize disseminated real‐time bus arrival information for pre‐trip passengers

In the advent of Advanced Traveler Information Systems (ATIS), the total wait time of passengers for buses may be reduced by disseminating real‐time bus arrival times for the next or series of buses to pre‐trip passengers through various media (e.g., internet, mobile phones, and personal digital assistants). A probabilistic model is desirable and developed in this study, while realistic distributions of bus and passenger arrivals are considered. The disseminated bus arrival time is optimized by minimizing the total wait time incurred by pre‐trip passengers, and its impact to the total wait time under both late and early bus arrival conditions is studied. Relations between the optimal disseminated bus arrival time and major model parameters, such as the mean and standard deviation of arrival times for buses and pre‐trip passengers, are investigated. Analytical results are presented based on Normal and Lognormal distributions of bus arrivals and Gumbel distribution of pre‐trip passenger arrivals at a designated stop. The developed methodology can be practically applied to any arrival distributions of buses and passengers.     

Effects of household structure and accessibility on travel

The concept of accessibility has been widely used in the transportation field, commonly to evaluate transportation planning options. The fundamental hypothesis of many studies related to accessibility could be “greater accessibility leads to more travel”. However, several studies have shown inconsistent results given this common hypothesis, finding instead that accessibility is independent of the trip/tour frequency. In addition, empirical aggregate urban modeling applications commonly produce either non-significant or negative (wrong sign) relationships between accessibility and the trip/tour frequency. For this reason, many practitioners rarely incorporate a measure of accessibility into trip/tour generation models out of consideration of the induced demand. In this context, this study examined the effect of accessibility in urban and suburban residences on the maintenance and discretionary activity tour frequencies of the elderly and the non-elderly using household travel survey data collected in the Seoul Metropolitan Area of Korea. The major finding of this study is that a higher density of land use and better quality of transportation service do not always lead to more tours due to the presence of intra-household interactions, trip chaining, and different travel needs by activity type. This finding implies that accessibility-related studies should not unquestioningly accept the common hypothesis when they apply accessibility measures to evaluate their transportation planning options or incorporate them into their trip/tour generation models.     

Effects of household structure and selected travel characteristics on trip chaining

This paper analyzes trip chaining, focusing on how households organize non-work travel. A trip chaining typology is developed using household survey data from Portland, Oregon. Households are organized according to demographic structure, allowing analysis of trip chaining differences among household types. A logit model of the propensity to link non-work trips to the work commute is estimated. A more general model of household allocation of non-work travel among three alternative chain types — work commutes, multi-stop non-work journeys, and unlinked trips — is also developed and estimated. Empirical results indicate that the likelihood of linking work and non-work travel, and the more general organization of non-work travel, varies with respect to household structure and other factors which previous studies have found to be important. The effects of two congestion indicators on trip chaining were mixed: workers who commuted in peak periods were found to have lower propensity to form work/non-work chains, while a more general congestion indicator had no effect on the allocation of non-work trips among alternative chains.