Impact of Variable Pricing on Temporal Distribution of Travel Demand

2001 ◽  
Vol 1747 (1) ◽  
pp. 36-43 ◽  
Author(s):  
Alasdair Cain ◽  
Mark W. Burris ◽  
Ram M. Pendyala
Keyword(s):  
Travel Demand ◽  
Temporal Distribution ◽  
Variable Pricing

Planning Lee County’s Variable-Pricing Program

1998 ◽  
Vol 1617 (1) ◽  
pp. 64-68 ◽  
Author(s):  
Mark Burris ◽  
Chris Swenson
Keyword(s):  
Travel Demand ◽  
Demand Management ◽  
The United States ◽  
Management Tool ◽  
Travel Demand Management ◽  
Lee County ◽  
Pricing Scheme ◽  
Variable Pricing ◽  
Toll Collection ◽  
Derived Data

Congestion (or variable) pricing can be described as the charging of more for goods or services during periods of peak demand. In theory, this practice would prove beneficial as a travel demand management tool when setting fares on toll roads and bridges. However, in practice, it has proven extremely difficult to implement in the United States. Many of the planning issues and solutions found during the early phases of a variablepricing pilot study currently being conducted in Lee County, Florida, are examined. Also examined is the electronic toll collection system being implemented in conjunction with variable pricing. The planned variablepricing scheme is detailed, along with how this publicly and politically palatable pricing scheme was derived. Data collection efforts, which include origin-destination surveys, focus groups, mail-back surveys, videotaping of traffic, and detailed traffic counts are described to emphasize the substantial effort undertaken to obtain accurate and meaningful variable-pricing traffic behavioral data that can be applied across the country. A summary of work to date and an overview of the next steps toward implementation of the variable-pricing program in Lee County are presented.

scholarly journals Comparative Analysis of Spatial–Temporal Distribution between Traditional Taxi Service and Emerging Ride-Hailing

2021 ◽  
Vol 10 (10) ◽  
pp. 690
Author(s):  
Di Wang ◽  
Tomio Miwa ◽  
Takayuki Morikawa
Keyword(s):  
Travel Demand ◽  
Temporal Distribution ◽  
Analytical Framework ◽  
Statistical Characteristics ◽  
Real Field ◽  
Tensor Factorization ◽  
Personal Mobility ◽  
Urban Structures ◽  
Taxi Service ◽  
Taxi Industry

The paradigms of taxis and ride-hailing, the two major players in the personal mobility market, are compared systematically and empirically in a unified spatial–temporal context. Supported by real field data from Xiamen, China, this research proposes a three-fold analytical framework to compare their mobilities, including (1) the spatial distributions of departures and arrivals by rank–size and odds ratio analysis, (2) the statistical characteristics of trip distances by spatial statistics and considering distance-decay effect, and (3) the meta-patterns inherent in the mobility processes by nonnegative tensor factorization. Our findings suggest that taxis and ride-hailing services share similar spatial patterns in terms of travel demand, but taxi demand heterogenizes more quickly with changes in population density. Additionally, the relative balance between the taxi industry and ride-hailing services shows opposite trends inside and outside Xiamen Island. Although the trip distances have similar statistical properties, the spatial distribution of the median trip distances reflects different urban structures. The meta-patterns detected from the origin–destination-time system via tensor factorization suggest that taxi mobilities feature exclusive nighttime intensities, whereas ride-hailing exhibits more prominent morning peaks on weekdays. Although ride-hailing contributes significantly to cross–strait interactions during daytime, there is a lack of efficient services to maintain such interactions at night.

Cross-Cultural Differences in the Temporal Distribution of Autobiographical Memory

2012 ◽  
Author(s):  
Steve M. J. Janssen ◽  
Anna Gralak ◽  
Yayoi Kawasaki ◽  
Gert Kristo ◽  
Pedro M. Rodrigues ◽  
...  
Keyword(s):  
Autobiographical Memory ◽  
Cultural Differences ◽  
Temporal Distribution ◽  
Cross Cultural ◽  
Cross Cultural Differences

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural
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