A comparative life-cycle energy and emissions analysis for intercity passenger transportation in the U.S. by aviation,intercity bus,and automobile

Intercity passenger trips constitute a significant source of energy consumption, greenhouse gas emissions, and criteria pollutant emissions. The most commonly used city-to-city modes in the United States include aircraft, intercity bus, and automobile. This study applies state-of-the-practice models to assess life-cycle fuel consumption and pollutant emissions for intercity trips via aircraft, intercity bus, and automobile. The analyses compare the fuel and emissions impacts of different travel mode scenarios for intercity trips ranging from 200 to 1600 km. Because these modes operate differently with respect to engine technology, fuel type, and vehicle capacity, the modeling techniques and modeling boundaries vary significantly across modes. For aviation systems, much of the energy and emissions are associated with auxiliary equipment activities, infrastructure power supply, and terminal activities, in addition to the vehicle operations between origin/destination. Furthermore, one should not ignore the embodied energy and initial emissions from the manufacturing of the vehicles, and the construction of airports, bus stations, highways and parking lots. Passenger loading factors and travel distances also significantly influence fuel and emissions results on a per-traveler basis. The results show intercity bus is generally the most fuel-efficient mode and produced the lowest per-passenger-trip emissions for the entire range of trip distances examined. Aviation is not a fuel-efficient mode for short trips (<500 km), primarily due to the large energy impacts associated with takeoff and landing, and to some extent from the emissions of ground support equipment associated with any trip distance. However, aviation is more energy efficient and produces less emissions per-passenger-trip than low-occupancy automobiles for trip distances longer than 700–800 km. This study will help inform policy makers and transportation system operators about how differently each intercity system perform across all activities, and provides a basis for future policies designed to encourage mode shifts by range of service. The estimation procedures used in this study can serve as a reference for future analyses of transportation scenarios.     

Relationship between proximity to transit and ridership for journey-to-work trips in Chicago

The use of privately owned vehicles (POVs) contributes significantly to US energy consumption (EC) and greenhouse gas emissions (GHGe). Strategies for reducing POV use include shifting trips to other modes, particularly public transit. Choices to use transit are based on characteristics of travelers, their trips, and the quality of competing transportation services. Here we focus on the proximity of rail stations to trip origins/destinations as a factor affecting mode choice for work trips. Using household travel survey data from Chicago, we evaluate the profile of journey-to-work (JTW) trips, assessing mode share and potential for more travelers to use rail. For work trips having the origin/destination as close as 1 mile from rail transit stations, POVs were still the dominant travel mode, capturing as much as 61%, followed by rail use at 14%. This high degree of POV use coupled with the proportion of JTW trips within close proximity to rail stations indicated that at least some of these trips may be candidates for shifting from POV to rail. For example, shifting all work trips with both the origin/destination within 1 mile of commuter rail stations would potentially reduce the energy associated with all work-related POV driving trips by a maximum of 24%. Based on the analysis of trips having the origin and destination closest to train stations, a complete shift in mode from POV to train could exceed CO₂ reduction goals targeted in the Chicago Climate Action Plan. This could occur with current settlement patterns and the use of existing infrastructure. However, changes in traveler behavior and possibly rail operation would be necessary, making policy to motivate this change essential.     

Electric vehicle park-charge-ride programs: A planning framework and case study in Chicago

In suburban areas, combining the use of electric vehicles (EV) and transit systems in an EV Park-Charge-Ride (PCR) approach can potentially help improve transit accessibility, facilitate EV charging and adoption, and reduce the need for long-distance driving and ensuing impacts. Despite the anticipated growth of EV adoption and charging demand, PCR programs are limited. With a focus on multi-modal trips, this study proposes a generic planning process that integrates EV infrastructure development with transit systems, develops a systematic assessment approach to fostering the PCR adoption, and illustrates a case implementation in Chicago. Specifically, this study develops a Suitability Index (SI) for EV charging locations at parking spots that are suitable for both EV charging and transit connections. SI can be customized for short-term and long-term planning scenarios. SI values are derived in Chicago as an example for (1) commuter rail stations (for work trips), and (2) shopping centers near transit stops as potential opportunities for additional weekday parking and EV charging (for multi-purpose trips/MPT). Furthermore, carbon emissions and vehicle miles travelled (VMT) across various travel modes and trip scenarios (i.e., work trips and MPT) are calculated. Compared to the baseline of driving a conventional vehicle, this study found that an EV PCR commuter can reduce up to 87% of personal VMT and 52% of carbon emissions. A more active role of the public sector in the PCR program development is recommended.     

An analysis of the methods used to calculate the emissions of rolling stock in the UK

The aim of this paper is to analyse and compare the methods used for calculating emissions of UK rolling stock based on their type and mode of operation. The three modes under comparison were; diesel, electric and bi-mode. As well as comparing these three modes of operation, a comparison between Conventional, Freight and High Speed Rail was made. Alternate fuels were considered for diesel and bi-mode locomotives and compared based on their environmental impact. The emissions of trains were studied using three methods. Specifically, the three chosen methods were used to calculate the emissions of each train and a comparison of these methods was made. In the current UK energy climate, diesel trains emit less emissions than electric trains when factoring in mechanical and air resistances. Bi-mode trains have their place in the UK network but with electrification of the network currently in place, this mode of operation will become redundant in the near future. High Speed Rail, although time efficient, releases high emissions due to energy consumption increasing with the square of speed. Alternative fuels, such as biodiesel, should be a consideration for the future of rail, as emissions fall dramatically with content of biodiesel in fuel blends.     

Modeling traveler mode choice behavior of a new high-speed rail corridor in China

This study examines mode choice behavior for intercity business and personal/recreational trips. It uses multinomial logit and nested logit methods to analyze revealed preference data provided by travelers along the Yong-Tai-Wen multimodal corridor in Zhejiang, China. Income levels are found to be positively correlated with mode share increases for high-speed rail (HSR), expressway-based bus, and auto modes, while travel time and trip costs are negatively correlated with modal shift. Longer distance trips trigger modal shifts to HSR services but prevent modal shift to expressway-based auto use due to escalation of fuel cost and toll charges. Travelers are less elastic in their travel time and cost for trips by nonexpressway-based auto use modes. The magnitude of elasticity for travel time is higher than trip costs for business trips and lower for personal/recreational trips. The study provides some policy suggestions for transportation planners and decision-makers.     

Applying a structured simulation-based methodology to assess carpooling time–space potential

Abstract

Increasing urban traffic congestion calls for the study of alternative measures. One such measure is carpooling, a system in which a person shares his private vehicle with one or more people in a commuter trip. In principle, this system could lead to potentially significant reductions in the use of private vehicles; however, in practice it has achieved limited success. In this paper, we apply a simulation-based methodology that uses aggregated data from commuter trips in an urban area to create compatible and feasible random trips. These are then analyzed through a heuristic process recursively to find grouping possibilities, thus producing indicators of carpooling potential such as the percentage of matched trips. Using this methodology, simulations are run for the Lisbon Metropolitan Area (Portugal) and results show that an increase in the number of participants in a carpooling scheme will only increase the probability of matching up to a certain point, and that this probability varies significantly with time–space trip attributes.     

Short trips: An opportunity for reducing mobile-source emissions?

This paper examines how conversion of automobile trips of less than 3 miles to other transportation modes reduces emissions. Short trips contribute disproportionately to emissions because of cold starts. An analysis is conducted of short-trip behavior across the US using the 1995 Nationwide Personal Transportation Survey. The data is used to develop likely scenarios of mode conversions for short trips, which are then applied to estimate emission savings using MOBILE6 cold start and running emission factors for volatile organic compounds, nitrogen oxides, carbon monoxide, and carbon dioxide. The results suggest that reducing short auto trips would modestly reduce mobile source air pollution, but emission reductions are high compared to most federally-funded surface transportation interventions aimed at improving air quality. Enhanced the community pedestrian environment to encourage short trip mode conversion also produces co-benefits such as increased physical activity and subsequent reductions in chronic diseases.     

The revenue and environmental benefits of new off-peak commuter rail service: the case of the Pascack Valley line in New Jersey

Although researchers have long argued in favor of off-peak transit service, studies that have empirically estimated its benefits regarding revenue generation, trip diversions, and greenhouse gas (GHG) emission are rare. This study provides important evidence about the benefits of off-peak commuter rail service by focusing on the Pascack Valley line in New Jersey, where off-peak service was introduced in October 2007. The research involved two focus groups and an onboard survey of passengers. Benefits were estimated regarding additional revenue generation and reduction in vehicle miles traveled (VMT) and GHG emission. The research shows that the new off-peak service potentially reduced VMT by more than 12 million annually due to diversions from other modes. Although diversions from other modes resulted in a substantial reduction in GHG emissions, due to the additional diesel fuel used by the new trains, the net GHG savings were in the range of 28–49 %. The research further shows that both peak period and off-peak riders benefited from the new off-peak service. Evidence is found about an increase in new transit riders and a modest increase peak period usage because of the off-peak service.     

The effects of ozone action day public advisories on train ridership in Chicago

Ground level ozone is a criteria pollutant that is significantly affected by transportation patterns. Ozone action day advisories represent one type of voluntary ozone-abating program operating in urban areas where ozone pollution is concentrated. When forecasts predict that ground level ozone will exceed healthy levels, public advisories urge citizens to voluntarily choose public transportation as a means of eliminating automobile trips and reducing mobile emissions. To obtain credit for emission reductions spurred by voluntary programs, states must provide verifiable reduction estimates. This paper applies a fixed effects regression model to a panel of hourly Chicago Transit Authority train ridership data to evaluate the potential effects of Ozone Action Day advisories in Chicago from 2002 to 2003. Findings show that while the overall effect of ozone action days on ridership is not significant, there are statistically significant changes in hourly ridership patterns that indicate a more complex relationship between the public advisories and travel behavior.     

Design charts for estimating transportation planning statistics from area population

Utilizing data collected for urban transportation studies in Ontario, regression analysis has been used to establish relationships between the daily number of person trips in an urban area and the area population. In particular the number of trips by auto drivers, auto passengers and mass transit riders have been investigated. Further, auto driver trips have been stratified into the following destination trip purposes: return home, work and related business, shopping, social-recreational and miscellaneous. The results of this analysis have been used to prepare a set of design charts. These charts are presented graphically and in the form of a nomogram. The accuracy of these charts has been investigated and found satisfactory for most planning purposes.     

The effect of neighbourhood characteristics,accessibility, home–work location,and demographics on commuting distances

The goal of this paper is to better understand home-to-work travel distances throughout the Montréal Metropolitan region. A simultaneous equation modelling analysis is carried out to jointly explain commuter trip length and home–work location as a function of neighbourhood typologies, commuter socio-demographics and measures of job and worker accessibility. First, a factor and cluster analysis of urban form is performed over the entire region on a fine-scale grid pattern. The outcome of this analysis is the classification of typologies at both home and job locations. Different measures of accessibility and commuter socio-demographics are then incorporated into the analysis. Varied data sources including a detailed Montréal Origin–Destination Survey on over 30,000 home-to-work automobile trips are analyzed. Among other results, commuters that live and work in a different sub-region almost double the average trip distance and although socio-economic factors have a statistically significant correlation with commuter distance, these factors have a marginal effect. Interestingly, our results highlight the importance of urban form and job accessibility. Deciding on whether to live and work in the same sub-region was modelled as an endogenous binary random utility model; unobserved heterogeneities seem to be simultaneously influencing both the home–work location choice and trip-to-work distances. Our results underscore the importance of home–work location with respect to urban form and job accessibility. Hence, policies that support more dense and mixed land-use in suburban areas would not be enough to reduce commuter distances. These actions should be accompanied by other policy initiatives to discourage long car trips.     

Making meaningful comparisons between road and rail – substituting average energy consumption data for rail with empirical analysis

Within the transport sector, modal shift towards more efficient and less polluting modes could be a key policy goal to help meet targets to reduce energy consumption and carbon emissions. However, making comparisons between modes is not necessarily straightforward. Average energy and emissions data are often relied upon, particularly for, rail, which may not be applicable to a given context. Some UK train operating companies have recently fitted electricity metres to their trains, from which energy consumption data have been obtained. This has enabled an understanding to be gained of how energy consumption and related emissions are affected by a number of factors, including train and service type. Comparisons are made with existing data for road and rail. It is noted that although more specific data can be useful in informing policy and making some decisions, average data continue to play an important role when considering the overall picture.     

A GPS-aided survey for assessing trip reporting accuracy and travel of students without telephone land lines

A geo-positioning satellite (GPS)-based survey, using a web-based prompted recall tool, was conducted on a sample of 94 students at the University of Toronto from November 2008 to April 2009. The sample included students with and without telephone land lines, allowing for a statistical comparison of demographic and travel behaviour attributes. The same subjects simultaneously completed a traditional trip reporting survey, modelled on the household travel survey in Toronto, allowing for a comparison between the travel behaviour information obtained from the GPS and that reported by the participants in the traditional survey. Students with a land line are more likely to live in houses, with parents, and to live in suburban areas than students without a land line. They also make fewer trips in total, fewer discretionary trips, more transit and auto trips and fewer active trips than students without a land line. By comparing questionnaire-based data and GPS data, we found that most participants reported in the questionnaire either the same number of GPS-based trips or fewer. On average, the GPS survey captured 1.29 more daily trips per participant than the corresponding trips reported in the questionnaire.     

Assessing the environmental impact associated with different trip purposes

We examine how trips with diverse motivations vary in their spatio-temporal characteristics and result in different impacts on the environment. An integrated model chain is used includes an activity-based traffic demand model, an emission model and a pollutant concentration module. The model setup is applied to the northern region of Belgium and analysed for NO₂, an important transport-related air pollutant. The results demonstrate that not every vehicle kilometer has the same impact on the environment in terms of emissions and concentration increases. We find that the highest concentration increase per kilometer is produced by work-related trips. Trips for shopping purposes and services produced the lowest marginal concentration increase. The difference between the highest and lowest marginal concentration increases is about 40%. Important explanatory factors include the time of day when a trip is made as well as vehicle type and speed.     

When strike comes to town… anticipated and actual behavioural reactions to a one-day,pre-announced,complete rail strike in the Netherlands

We conducted secondary analysis on data collected among rail users, days before and after a national rail strike in the Netherlands. Our aim was to compare anticipated and actual behavioural reactions to the rail strike, investigate associations with traveller and trip characteristics, and perceived behavioural control and satisfaction with the chosen alternative. Forty-four percent of the people who had anticipated to travel by train on the day of the strike abandoned their trip, 24% switched to car as driver, 14% switched to another mode (as passenger), 18% stayed with the train and rescheduled the planned activity to another day. Almost half of people who had anticipated travelling by car expected to change behaviour as well. Multinomial logistic regression showed low preference for car among rail users. Considerable marginal effects were found for several variables, e.g.: young people and females were less likely to switch to car; short and middle distance trips were less likely abandoned or switched to another day; commute and business trips were more likely done by car, and business trips less likely cancelled. Despite high levels of perceived behavioural control and satisfaction with the chosen alternative, permanent modal shift as result of this strike is not expected.     

Emissions of particulate matters from railways – Emission factors and condition monitoring

We report tunnel measurements, where abrasion particles from different types of trains are measured in two situations and emission factors are calculated from these. The measurements show significantly elevated particle concentrations in train passages that last for about a minute and are accompanied by increased airflow. The emission of particles for each train passage was obtained by summing the product of the particle concentration and the airflow and then multiplying with the tunnel area. The average emission factors were calculated for three categories freight trains, commuter trains and regional trains and found to be 2.9 g/train-km (0.7 – 9), 0.48 (0.1 – 1.6) and 0.24 (0.05–0.9), respectively. The number in brackets refers to the range of values observed. The size-resolved data showed maxima around 1 μm for all three train types.     

A comparative analysis of traditional four-step and activity-based travel demand modeling: a case study of Tampa,Florida

Activity-based travel demand modeling (ABTDM) has often been viewed as an advanced approach, due to its higher fidelity and better policy sensitivity. However, a review of the literature indicates that no study has been undertaken to investigate quantitatively the differences and accuracy between an ABTDM approach and a traditional four-step travel demand model. In this paper we provide a comparative analysis against each step – trip generation, trip distribution, mode split, and network assignment – between an ABTDM developed using travel diary data from the Tampa Bay Region in Florida and the Tampa Bay Regional Planning Model (TBRPM), an existing traditional four-step model for the same area. Results show salient differences between the TBRPM and the ABTDM, in terms of modeling performance and accuracy, in each of the four steps. For example, trip production rates calculated from the travel diary data are found to be either double or a quarter less than those used in the TBRPM. On the other hand, trip attraction rates computed from activity-based travel simulations are found to be either more than double or one tenth less than those used in the TBRPM. The trip distribution curves from the two models are similar, but both average and peak trip lengths of the two are significantly different. Mode split analyses show that the TBRPM may underestimate driving trips and fail to capture any usage of alternative modes, such as taxi and nonmotorized (e.g., walking and bicycling) modes. In addition, the ABTDMs are found to be less capable of reproducing observed traffic counts when compared to the TBRPM, most likely due to not considering external and through trips. The comparative results presented can help transportation engineers and planners better understand the strengths and weaknesses of the two types of model and this should assist decision-makers in choosing a better modeling tool for their planning initiatives.     

Traditional neighborhoods and commuting in the San Francisco Bay area

Neo-traditional designs, proponents argue, reduce dependency on the automobile and provide attractive environments for walking, bicycling, and transit riding. This paper explores the extent to which this proposition holds for seven traditional neighborhoods in the San Francisco Bay Area that evolved around early streetcar services. Matched-pair comparisons of modal shares and trip generation rates for work trips are made between these neighborhoods and newer auto-oriented suburbs, controlling for the effects of income and, to a lesser extent, existing bus service levels. Pedestrian/bicycle modal shares and trip rates tended to be considerably higher, in some cases five time as high, in transit-oriented than in the paired auto-oriented neighborhood. Transit neighborhoods also averaged around 70 more daily transit work trips per 1,000 households than auto-oriented neighborhoods, though trip rates varied considerably among neighborhood pairs. Higher residential densities were also found to have a proportionately greater impact on transit commuting in transit-oriented than in auto-oriented neighborhoods. The paper concludes that in order to yield significant transportation benefits, neo-traditional development must be coordinated with larger regional planning efforts and public policy initiatives to reduce automobile dependency.     

Comparing rail fuel efficiency with truck and waterway

This paper presents a railroad energy efficiency model used to estimate the fuel economies for classes of trains transporting various commodities. Comparable procedures are used to estimate truck and waterway fuel consumption. The results show that coal unit trains are 4.5–5.0 times more energy efficient than movements in the largest trucks allowed in the eastern and western regions of the US, unit grain train movements in the central US are 4.6 times more fuel efficient, soda ash unit train and non-unit train shipments are 4.9 and 3.2 times more efficient, and ethanol unit train and non-unit train movements are 4.8 and 3.0 times more efficient. In terms of barge traffic, coal unit train and non-unit train are 1.3 and 0.9 times as energy efficient in the eastern US, grain unit train and non-unit train movements are 1.7 and 1.0 times more efficient from Minneapolis to the Gulf of Mexico, and grain unit train and non-unit train movements are 1.0 and 0.7 times more fuel efficient from the Upper Ohio River to the Gulf of Mexico.     

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.