Journey time monitoring by ANPR, and the implications of larger scale systems

2004 ◽  
Author(s):  
T. Cooper
Keyword(s):  
Journey Time

Transportation: Fuel Energies

2017 ◽  
Author(s):  
Peter Rez
Keyword(s):  
Energy Density ◽  
Fossil Fuels ◽  
High Energy ◽  
Ease Of Use ◽  
Low Energy ◽  
High Energy Density ◽  
Nuclear Fuels ◽  
Liquid Hydrocarbons ◽  
Transportation Fuel ◽  
Journey Time

Transportation efficiency can be measured in terms of the energy needed to move a person or a tonne of freight over a given distance. For passengers, journey time is important, so an equally useful measure is the product of the energy used and the time taken for the journey. Transportation requires storage of energy. Rechargeable systems such as batteries have very low energy densities as compared to fossil fuels. The highest energy densities come from nuclear fuels, although, because of shielding requirements, these are not practical for most forms of transportation. Liquid hydrocarbons represent a nice compromise between high energy density and ease of use.

scholarly journals Quantifying the effects of passenger-level heterogeneity on transit journey times

2020 ◽  
Vol 1 ◽  
Author(s):  
Ramandeep Singh ◽  
Daniel J. Graham ◽  
Richard J. Anderson
Keyword(s):  
Large Scale ◽  
Semiparametric Regression ◽  
Related Factors ◽  
Network Characteristics ◽  
Journey Time ◽  
Time Variance ◽  
Assignment Algorithm ◽  
Rail Systems ◽  
Urban Rail ◽  
Potential Applications

Abstract In this paper, we apply flexible data-driven analysis methods on large-scale mass transit data to identify areas for improvement in the engineering and operation of urban rail systems. Specifically, we use data from automated fare collection (AFC) and automated vehicle location (AVL) systems to obtain a more precise characterisation of the drivers of journey time variance on the London Underground, and thus an improved understanding of delay. Total journey times are decomposed via a probabilistic assignment algorithm, and semiparametric regression is undertaken to disentangle the effects of passenger-specific travel characteristics from network-related factors. For total journey times, we find that network characteristics, primarily train speeds and headways, represent the majority of journey time variance. However, within the typically twice as onerous access and egress time components, passenger-level heterogeneity is more influential. On average, we find that intra-passenger heterogeneity represents 6% and 19% of variance in access and egress times, respectively, and that inter-passenger effects have a similar or greater degree of influence than static network characteristics. The analysis shows that while network-specific characteristics are the primary drivers of journey time variance in absolute terms, a nontrivial proportion of passenger-perceived variance would be influenced by passenger-specific characteristics. The findings have potential applications related to improving the understanding of passenger movements within stations, for example, the analysis can be used to assess the relative way-finding complexity of stations, which can in turn guide transit operators in the targeting of potential interventions.

scholarly journals Data gold mining: MIDAS and journey time predictors

Significance ◽  
2005 ◽  
Vol 2 (3) ◽  
pp. 102-105 ◽  
Author(s):  
Richard Gibbens ◽  
Wiebke Werft
Keyword(s):  
Gold Mining ◽  
Journey Time

scholarly journals Journey time estimation using single inductive loop detectors on non-signalised links

2002 ◽  
Vol 53 (6) ◽  
pp. 610-619 ◽  
Author(s):  
T Cherrett ◽  
F Mcleod ◽  
H Bell ◽  
M McDonald
Keyword(s):  
Time Estimation ◽  
Inductive Loop ◽  
Journey Time ◽  
Loop Detectors

scholarly journals Analysis of public transport connections along the railway line No. 406 on the section Szczecin Główny – Police

2018 ◽  
Vol 2018 (2) ◽  
pp. 22-31
Author(s):  
Karol F. Abramek ◽  
Paweł Regulski
Keyword(s):  
Travel Time ◽  
Public Transport ◽  
Travel Times ◽  
Railway Stations ◽  
Journey Time ◽  
Railway Line ◽  
General Manner

The article presents an analysis of selected public transport lines running along the railway line Szczecin Główny – Police. Examined journey time by public transport between the railway stations and stops. Compared to the travel time by train and passenger public transport vehicles. In addition, a comparison of planned and actual travel times of public transport vehicles. In a general manner specified number of passenger public transport.

Does the transport stocking density of 30-kg live weight pigs affect their heart rate and choice of posture?

1997 ◽  
Vol 20 ◽  
pp. 102-103 ◽  
Author(s):  
H. L. Riches ◽  
H. J. Guise
Keyword(s):  
Heart Rate ◽  
Stocking Density ◽  
Live Weight ◽  
Journey Time ◽  
Commercial Practice ◽  
European Directive ◽  
Common Weights ◽  
Young Pigs ◽  
The Mean

There is currently no published information on the conditions under which young pigs are transported. European Directive 95/29/EC concerns the protection of animals during transport but recommends no stocking density for pigs moved by road other than those of 100 kg live weight. A survey of 97 farms showed store pigs accounted for 64% of all inter-farm journeys. The most common weights were 26 to 30 kg. The mean stocking density for transport was 162kg/m2 (range 72-214 (s.d. 25) kg/m2). The mean journey time was 165 min and the distance 228 km. This experiment was designed within the range of stocking densities and distances travelled found in current commercial practice. Pigs were transported at high, medium and low densities. Posture and heart rate during transport were recorded.

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