Peak Travel Research Articles

Buckling up in Singapore: residency and other risk factors for seatbelt non-compliance - a cross-sectional study based on trauma registry data.

BackgroundSeatbelt non-compliance is a problem in middle income countries, and little is known about seatbelt compliance in populations with a high proportion of non-residents. This study analyses the profile of seatbelt non-compliance in Singapore based on trauma registry data from five of the six public hospitals.MethodsThis is a cross-sectional study of seatbelt compliance of patients aged over 18 years, attending the emergency departments of five public hospitals in Singapore after road collisions from 2011–2014. Seatbelt data was obtained from paramedic and patient history.ResultsThere were 4,576 patients studied. Most were Singapore citizens (83.4 %) or permanent residents (2.4 %), with the largest non-resident groups from Malaysia, India, and China. Overall seatbelt compliance was 82.1 %.On univariate analysis, seatbelt compliance was higher in older patients (OR 1.02, 95 % CI 1.001–1.021, p < 0.0001); drivers, followed by front passengers (OR 0.65, 95 % CI 0.51–0.83, p < 0.0001), were more compliant than rear passengers (OR 0.08, 0.06–0.09, p < 0.0001); occupants of larger vehicle types (buses, heavy transport vehicles, minibuses and vans) were more non-compliant compared to occupants of private cars and taxis. Morning peak travel (0700 h-0900 h) and being a non-resident were other risk factors for non-compliance.On multivariable analysis, older age (OR 1.01, 95 % CI 1.001–1.014, p = 0.03) was associated with compliance, while non-residents from China (OR 0.43, 95 % CI 0.18–0.99, p = 0.05), seat position (front passenger compared to driver, OR 0.64, 95 % CI 0.48–0.85, p = 0.002; rear passenger compared to driver, OR 0.067, 95 % CI 0.05–0.09, p < 0.0001), vehicle type (bus compared to car, OR 0.04, 95 % CI 0.017–0.11, p < 0.0001, van compared to car, OR 0.55, 95 % CI 0.36–0.83, p = 0.004), and travel at morning peak periods were independent predictors of seatbelt non-compliance.When the sub-group of drivers was analysed, only vehicle type was a significant predictor of seatbelt compliance, with bus drivers least likely to be compliant to seatbelts (multivariable analysis, OR 0.057 compared to cars, 95 % CI 0.019–0.18, p < 0.0001).ConclusionsWhile overall seatbelt compliance in our study is high, efforts can be made to increase compliance for morning rush hour passengers, rear seat passengers, and occupants of buses, heavy transport vehicles, and vans or pickups.

Adaptive imaging processing for reducing ultrasonic ringdown interference pattern

In the oil and gas industry, ultrasonic imaging tools are actively used in stress identification and fracture detection. Pulse-echo transducers measure the properties of the echoes reflected from the formation, and the signal peak amplitude and travel time are typically displayed as a circumferential image of the borehole wall. A well-known problem is that the pulse-echo transducers have internal ringdown which constantly modulates formation echoes. As a result, the acquired image is often dominated by a “wood-grain” interference pattern which degrades image quality. Traditional ringdown reduction methods try to remove the ringdown effect by estimating the ringdown waveform. However, the ringdown signal varies versus borehole conditions, which make it inadequate for traditional methods to efficiently reduce the image interference pattern. In this paper, we proposed an image processing method which significantly reduces the interference pattern. This new image processing method does not require any ringdown waveforms. It adaptively estimates the ringdown modulation template and after that constructs the interference amplitude pattern. Examples shows promising results and have proven the propose method is more efficient than traditional ringdown reduction methods on the ringdown interference pattern reduction.

Exploring Recent Long-distance Passenger Travel Trends in Europe

This paper reviews recent trends in long-distance passenger travel in Europe. Its purpose is to explore whether some early signals for peak travel in long-distance transport can be identified and if so, which could be the options for a consistent policy action. For the sake of simplicity, only car and air modes are considered.The analysis is based on previous research conducted for the preparation of the recent report of the European Environment Agency (EEA), “Focusing on environmental pressures from long-distance transport - TERM 2014” (EEA, 2014). Passenger travel demand data at the national level show striking differences among European countries. Beyond the traditional divide between “new” and “old” EU member states, it seems that differences respond to a much richer variety of traits, including the size of the country, per capita GDP trends and the characteristics of the population. It is within this complex geographical background that the influence of three traditional key drivers should be explored: population, disposable income and lifestyles.Although there are good arguments to conclude that peaking in long-distance transport demand could be reached in an increasing number of European countries, there are also significant forces to further expand demand. Furthermore, it is worth highlighting that peaking, if materialised, would be happening at levels too high to be compatible with GHG emission reduction targets.The paper concludes that there are positive signs in the recent trends of these drivers indicating that they would not result in additional mobility demand. However, these trends could be neutralised by other factors, such as the strategies of European transport policy makers and operators to foster latent demand and expand their markets, this is illustrated by current strategies of air travel operators and some regional governments to further develop low-cost, regional airport-based services.

Impact of New Tolls on Existing Roadway Facilities: Hampton Roads, Virginia, Experience

The Midtown and Downtown Tunnels have been two of the most congested facilities in the Hampton Roads area of Virginia. Traffic queues nearly 4 mi long were daily occurrences at both facilities during peak travel periods. To relieve this congestion, construction began in 2012 of an additional two-lane tube at the Midtown Tunnel, along with rehabilitation of the Downtown Tunnel and the original Midtown Tunnel, and the addition of an extension to the Martin Luther King Freeway. To finance the project, whose completion is expected in 2018, toll collection began at the Midtown and Downtown Tunnels on February 1, 2014. The Hampton Roads Transportation Planning Organization (HRTPO) studied the impact of tolling on the regional transportation system by comparing the traffic and transit conditions before and after toll collection was implemented at the Midtown and Downtown Tunnels. This research will assist local decision makers as they determine whether tolls should be included as a funding source for candidate major highway projects. Further, the research will provide other planning agencies with information about the regional travel impacts of tolling, given that tolls were implemented on existing facilities before capacity improvements were completed. This study included an analysis of changes in traffic volumes, queues, delays, and transit conditions and highlighted a survey funded by the HRTPO to assess the public’s views of the project. Among the findings was that peak period delays decreased by 32% at four river-crossing corridors after tolls were implemented.

Agent-based simulation on tourists’ congestion control during peak travel period using Logit model

During the peak travel period, the high concentration of tourists in a short time results in great negative influence for the scenic areas. Therefore, increasing attention from both scholars and practitioners of tourism has been paid to the tourists shunt management model during peak travel period, however, the traditional shunt strategies may cause the Matthew Effect, a complex system effect. This paper introduces the Logit model on the basis of the preliminary study, to evaluate the performance of the proposed method, two evaluation indexes of tourists balanced distribution including the variance model and the Gini–Simpson are introduced. Furthermore, the paper takes Jiuzhaigou as an example and constructs the Jiuzhaigou tourist shunt simulation model based on multi-agent simulation platform of NetLogo. Numerical simulations show that the effects based on Logit model is superior to the preliminary study.

Long-term trends in tourism climate index scores for 40 stations across Iran: the role of climate change and influence on tourism sustainability.

Tourism is a rapidly growing international sector and relies intrinsically on an amenable climate to attract visitors. Climate change is likely to influence the locations preferred by tourists and the time of year of peak travel. This study investigates the effect of climate change on the Tourism Climate Index (TCI) for Iran. The paper first calculates the monthly TCI for 40 cities across Iran for each year from 1961 to 2010. Changes in the TCI over the study period for each of the cities are then explored. Increases in TCI are observed for at least one station in each month, whilst for some months no decreases occurred. For October, the maximum of 45% of stations demonstrated significant changes in TCI, whilst for December only 10% of stations demonstrated change. The stations Kashan, Orumiyeh, Shahrekord, Tabriz, Torbat-e-Heidarieh and Zahedan experienced significant increases in TCI for over 6 months. The beginning of the change in TCI is calculated to have occurred from 1970 to 1980 for all stations. Given the economic dependence on oil exports, the development of sustainable tourism in Iran is of importance. This critically requires the identification of locations most suitable for tourism, now and in the future, to guide strategic investment.

Investigating empirical implications of hysteresis in day-to-day travel time variability

Day-to-day travel time variability plays a significant role in travel time reliability. Nowadays, travelers not only seek to minimize their travel time on average, but also value its variation. The variation in the mean and the variance of travel time (across days, for the same departure time) has not been thoroughly investigated. A temporary decrease in capacity (e.g. congestion caused by an active bottleneck) leads to a quite significant difference in the variance of travel time for congestion onset and offset periods. This phenomenon results in hysteresis loops where the departure time periods in congestion offset exhibit a higher travel time variance than the ones in congestion onset with the same mean travel time. The aim of this paper is to identify empirical implications that yield to the hysteresis phenomenon in day-to-day travel times. First, empirical hysteresis loop observations are provided from two different freeway sites. Second, we investigate the potential link with the hysteresis observed in traffic networks on macroscopic fundamental diagram (MFD). Third, we build a piecewise linear function that models the evolution of travel time within the day. This allows us to decompose the problem into its components, e.g. start time of congestion, peak travel time, etc. These components, along with their probability distribution functions, are employed in a Monte Carlo simulation model to investigate their partial effects on the existence of hysteresis. Correlation among critical variables is the most influential factor in this phenomenon, which should be further investigated regarding traffic flow and traffic equilibrium principles.

Effect of Cascade Reservoirs on Flood Routing in the Hongshui River, Southern China

Hongshui River, with a length of 659 km and drainage area of 130,870 km 2 , is the upstream reach of Xijiang River, which is the dominant branch river of the Pearl River system in Southern China. There are 9 dams with a total storage capacity of 384.9×10 8 m 3 constructed on the Hongshui River and its upstream reach Nanpan River since 1980s (the annual water discharge of Hongshui River is 696×10 8 m 3 ). The water level of the 9 reservoirs is connected together, which causes the riverbed channelizing and the flood characteristic changing during the processes of flood routing. To study the effect of the cascade reservoirs on flood routing, 363 flood peaks data of two hydrologic stations on the Hongshui River during 1971–2007 are collected to analyze the flood characteristics changing before or after construction of the large dam Yantan. The flood peak travel time is the main factor which is taken into consideration in this study. Statistics results show: (1) The flood peak travel time which varies with flood magnitude shows different trends before and after construction of the Yantan dam; (2) the average flood peak travel time with a smaller flood magnitude ( P=50% ) is shorter in the post-Yantan dam period than in the pre-Yantan dam period. However, the flood peak travel time with a relatively larger flood magnitude (> Q P=50% ) has no obvious differences before and after the construction of Yantan dam.

Who, What, When, and Where

Public transportation agencies are faced with the difficult task of providing adequate service during peak travel periods while maintaining adequate service for those traveling off-peak or outside a city or region's densest areas. The ability or inability of a transit system to meet these needs helps explain transit ridership rates. This research sought to understand how daily fluctuations in transit service were related to ridership in the greater Toronto and Hamilton area, in Canada, for different segments of the labor force. Many variables—including frequency and proximity of transit service, socioeconomic status, the built environment, and accessibility to employment through transit—have been linked to transit use in past research. However, many previous studies focused only on travel during peak hours. This study investigated whether fluctuations in service and demand were related to transit ridership rates. With the use of six time periods, an improved understanding of daily variation in transit mode share for commuting trips was produced. With a further division of the commuting population into two employment wage categories, it was demonstrated that the common understanding of the influences on transit ridership was potentially misleading. Commuting transit mode share and the variables that influence it are intimately related to when travel is needed and to what jobs people are traveling. To encourage transit use, agencies and researchers need to take into account commuters’ need to commute at a variety of time periods.

Using Digital Footprints for a City-Scale Traffic Simulation

This article introduces a microsimulation of urban traffic flows within a large-scale scenario implemented for the Greater Dublin region in Ireland. Traditionally, the data available for traffic simulations come from a population census and dedicated road surveys that only partly cover shopping, leisure, or recreational trips. To account for the latter, the presented traffic modeling framework exploits the digital footprints of city inhabitants on services such as Twitter and Foursquare. We enriched the model with findings from our previous studies on geographical layout of communities in a country-wide mobile phone network to account for socially related journeys. These datasets were used to calibrate a variant of a radiation model of spatial choice, which we introduced in order to drive individuals’ decisions on trip destinations within an assigned daily activity plan. We observed that given the distribution of population, the workplace locations, a comprehensive set of urban facilities, and a list of typical activity sequences of city dwellers collected within a national travel survey, the developed microsimulation reproduces not only the journey statistics such as peak travel periods but also the traffic volumes at main road segments with surprising accuracy.

A GEV model with scale heterogeneity for investigating the role of mobility tool ownership in peak period non-work travel mode choices

The paper presents results of investigation on mode choice behaviour for peak period non-work trips (trips that are made during the peak period and are not linked to any other trips). Pure non-work trips within the peak period represent a significant portion of peak period traffic. In the case of the Greater Toronto and Hamilton Area (GTHA), the study area of this investigation, around 11 percent of peak period trips are pure non-work trips and auto driving is the dominant mode. Also more peak period non-work trips are made using the auto passenger mode than all transit modes combined. This heavy auto dependency for pure non-work trips in the peak period, when transit service is at its highest throughout the day, requires an improved understanding of such mode choice behaviour. This paper uses data from a household travel survey collected in the GTHA to investigate peak period pure non-work trip mode choice in the context of household mobility tool ownership (auto and transit pass ownership). The paper also proposes an advanced econometric modelling approach for capturing preference heterogeneity as well as scale heterogeneity (heteroskedasticity). The model involves systematic parameterization of the scale parameter of a GEV model. Empirical models highlight the superiority of the proposed model over a homoskedastic model. Empirical model also explains the influence of household mobility tool ownership on peak period pure non-work trip mode choices in terms of explaining both preference heterogeneity and scale heterogeneity.

Above and below: measuring crime risk in and around underground mass transit systems

This paper explores crime risk within and around major transit systems, specifically by investigating theft of personal property offences on the London Underground. The majority of studies to date have examined theft above ground, predominantly at transit stations, although some studies have compared this with theft in nearby surrounding areas. This study is unique and extends this analysis to theft during transit journeys below ground. The location of such offences is often unknown, only discovered by the victim sometime after the event. A new technique termed Interstitial Crime Analysis is used to better measure the location of below ground theft offences; these are compared with above ground thefts using Spearman’s Rank tests for association. Key findings are: below ground theft offences are concentrated at particular stations; risk is highest during morning and late afternoon peak travel periods; at these peak times there is an elevated risk of theft at both high risk stations and in their surrounding environs; and that this relationship is not evident during the inter-peak and late night time periods. The findings suggest offenders who operate below ground may also operate above ground on major transit systems. This has clear policy implications for policing these settings and highlights the importance of joint operations and information sharing between transit agencies and local police forces operating near major transit systems.

Peak Travel and the Decoupling of Vehicle Travel from the Economy

Decades of growth in overall and per capita automobile use led many to believe that driving-rate increases would occur indefinitely. In the mid-2000s, driving levels in the United States and other developed countries peaked and then began to decline. Referred to as “peak travel,” this international phenomenon is occurring in places with urban layouts, densities, and demographics that are quite different from one another and suggests a fundamental shift in travel behavior. Simultaneously, after 70 years of concurrent growth, the complex relationship between the economy (as measured by gross domestic product) and personal vehicle travel appears to be changing, and this change suggests a weakening connection between the two. This paper reviews the literature about the current understanding and potential causes of these revolutionary trend reversals. Although causes such as saturation of demand, aging, decline of young drivers, preference shifts, and time budget constraints all contribute to reduced automobile travel at one time or another, or in one place or another, none of these factors can explain why peak travel is occurring on multiple scales in a diversity of places. The authors conclude that although the existing literature explains the recent trend reversal in specific cities or partially explains the global phenomenon, the fundamental reasons for peak travel are still not understood. Further, the authors challenge fellow researchers to explain these phenomena for more accurate and efficient planning of the transportation infrastructure.

Examination of Paid Travel on I-85 Express Lanes

The implementation of the I-85 express lanes in Atlanta, Georgia, was the first conversion of a high-occupancy vehicle lane to a high-occupancy toll lane that simultaneously increased the occupancy requirement (from two or more persons to three or more persons) and implemented tolls without the creation of additional lanes. This paper presents the findings of an analysis of tolling data collected during the first year of operation as part of the National Evaluation of Urban Partnership Agreement and Congestion Reduction Demonstration programs sponsored by the U.S. Department of Transportation. This analysis found the median willingness to pay for express lane travel time savings to be $19.45 per hour across all time periods and $33.17 per hour for southbound a.m. peak travel. There was little difference in the willingness to pay between frequent and infrequent travelers of the express lanes. This analysis was based on actual tolls paid by travelers and therefore represents the minimum these travelers were willing to pay.

Airport Cities and Airport Public Transport Access

Many airports are adding nonaviation activities in an effort to diversify and become less dependent on direct air traffic revenues and thus create so-called airport cities. As these added activities generate an increase in traffic, the question arises as to how this increased traffic influences the ground transport access system and if this influence can be used in strategies to achieve higher public transport mode shares. In the best case, the additional traffic would follow a distribution that would balance out the peaks created by the aviation-induced travel and therefore would help create more even transport demand. Such a more balanced situation is especially critical for providing cost-efficient public transport services where service capacity cannot be adjusted very flexibly without sacrificing ease of use. In the worst case, however, additional nonaviation travel demand would peak at the same time as aviation-induced travel and thus increase peak period travel demand. This effect would increase costs for peak period service without improving utilization during low-demand periods. This paper presents a generic framework for analyzing aviation and nonaviation transport demand, followed by a specific study of Zurich Airport in Switzerland. In this case, the nonaviation demand is distributed as complementary to the aviation-induced demand, but the volume of nonaviation demand is so high that it dominates the total demand curve. This result means that the total travel demand is high enough to support a higher level of public transport service than could be operated economically if fed only by the aviation operations and is one reason why Zurich Airport's public transport mode share for air travelers is approximately 50%.

Research on Passenger Capacity of Escalator in the Hub

Transportation hub is the highly crowd gathering place, and escalatorwith the hub as the main carrier of passengers’ transferring, pitting and otheractivities, it can ensure efficient flow of pedestrians. In recent years, theutilization of escalators in the building and medium-sized transport hubimproved significantly. Its passenger capacity will directly affect theevacuation of pedestrian flow, and it is an important factor in the escalator.Based on the escalator passenger capacity study of the Hohhot Railway station ,by analyzing the relationships among speed ,density and flow of the pedestrianwho are carried by escalator within the hub in the peak travel period, wecalculate with the actual carrying capacity of the escalator to arrive at anescalator utilization within the hub. And according to the level of utilizationwe submit reasonable proposals to achieve the purpose of improving the hub.

Who Are the Drivers of Peak Car Use?

This study investigates the contribution of the aging of the population and changes in travel behavior by age group to peak car use in France, Germany, Great Britain, Japan, Norway, and the United States. The terms “peak car use,” “peak car travel,” and “peak car hypothesis” have been coined for the recent trend reversal in car travel development observed in some industrialized countries. In the study countries, car travel was characterized by growth for a long time but started to show signs of stagnation or even decrease in the past decade. The authors analyze underlying travel trends since the mid-1990s and use a trend decomposition based on descriptive statistics from national travel surveys and Laspeyres indices. The results indicate that relevant developments have different weight in shaping peak car use in the study countries. In many places, the aging of the population has been an important contributor to peak car use. In Japan, aging was the most important factor limiting growth of car travel. In all study countries except the United States, where car ownership by seniors has not grown, increasing car ownership and car use by seniors have contributed to increasing car travel and thus have exerted a damping influence on peak car use. Another important development was new travel trends of young adults. In three countries, the contribution of young adults to peak car use is crucial: if young adults' car ownership and car mode share had not decreased, results would not have shown declining total car kilometers per capita in Germany and would have shown much stronger increases in Great Britain and Norway.

Tourist management based on behaviour characteristics during peak travel period - a case study of Jiuzhaigou

During the peak travel period, the high concentration of tourists in a short time results in great negative influence for the scenic areas, therefore, more and more researchers from all research areas have paid attention to the protection of the environment of scenic areas for the scarce resources. Traditional researches mainly take the stand of administrators and neglect the tourist’s behaviour characteristics. Surveys indicate that tourists perform specific behaviour characteristics during their journey, including choosing travel destinations according to personal preference, avoiding crowded scenic spots, and making the scenic spot, which is nearer, their first choice. This paper proposes shunt strategies based on the three behaviour characteristics mentioned above. With Jiuzhaigou as a case study, the simulation model for Jiuzhaigou tourists’ shunt is made. On the basis of this model, the effectiveness of the previous three schemes can be compared. Then, the PCD coordinate coefficient, which can comprehensively reflect the previous three behaviour characteristics, is built. Through the simulation contrast, the strategy based on this coordinate coefficient shows comparatively good features.

Departure Time Choice Analysis Using Ordered Response Probit Model: Journey to Work Trip in Dhaka City

With growing concern about travel time increase and energy consumption, transportation planners and decision makers are becoming gradually more conscious of the necessary reallocation of the departure time at the peak period travel in an effort to reduce traffic congestion, environmental emission and peak load. The paper addresses this particular problem through the development and estimation of a journey to work (JTW) departure time choice model. The model has been developed by considering five characteristics, such as individual socio-demographics, household socio demographic characteristic, employment related attributes, trip related attributes and arrival related aspects. By identifying departure time choice, one can observe the level of earlier start as an ordinal variable that expresses extremity of earlier start behavior which is increased with larger values of departure time. To represent this ordinal variable, an ordered response probit (ORP) model has been employed to investigate the JTW trips in Dhaka city. Journal of Bangladesh Institute of Planners Vol. 5, Dec 2012, pp. 107-118

Commuter’s behavioral differences in activity generation and scheduling between the activity types

In order to analyze the trip generation behavior of metropolitan commuters, a two-stage structure is proposed in which an activity demand generation model is constructed and the generated activity demand is connected with the other trips. As for the activity generation model, the frequency of activities generated was constructed into an ordered logit model, and the trip chain was constructed using a multinomial logit model by focusing on the connectivity with the commute trip. We applied these model structures to the case of Seoul metropolitan, and we found that generating activities and chaining were determined by means of the interactivity among the activities. Also, we found that the behavioral dissimilarity between the activity types was revealed. These results can be used to forecast future travel demand more precisely based on demographic changes, especially for peak travel times.