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This paper provides a day-to-day analysis of the reliability of commuting time and trip scheduling under the Advanced Traveler Information System (ATIS). A simple network with parallel routes and bottleneck congestion is used to simulate the departure time and route choice decisions of commuters to minimize total travel time and scheduling delay cost. There are two major factors influencing the decisions of drivers in their departure time and route choices: their accumulated travel experience and information provided by ATIS. A simple experiment is carried for investigating trip-scheduling reliability of this network system.

The sole purpose of this simple paper is to present the idea that theorists of consumption, orthodox or otherwise, might do well to focus their attention on the use of time. ‘Economics is at bottom the study of how humans spend their lifetimes’ (Georgescu-Roegen, 1983, p. lxxxv), after all, and it thus makes sense to place time-use at centre-stage and to make sure that it is considered explicitly within consumer theory. Such an emphasis, it will be urged, enables the economic theorist to connect more easily both with certain other social-scientific and philosophical concerns and with many everyday common-sense concerns. ‘What shall I do?’, for example, is both a more frequent and a deeper question than, ‘What shall I buy/consume?’ (‘What ought I to be?’ is no doubt a still deeper – but less frequent! – question but is too difficult to be considered here.)

This chapter has been seminal work of Dr K.S.S. Iyer, which has taken time to develop, for over the last 56 years to be presented here. The method in advance predictive analytics has developed, from his several other applications, in predictive modeling by using the stochastic point process technique. In the chapter on advance predictive analytics, Dr Iyer is collecting his approaches and generalizing it in this chapter. In this chapter, two of the techniques of stochastic point process known as Product Density and Random point process used in modelling problems in High energy particles and cancer, are redefined to suit problems currently in demand in IoT and customer equity in marketing (Iyer, Patil, & Chetlapalli, 2014b). This formulation arises from these techniques being used in different fields like energy requirement in Internet of Things (IoT) devices, growth of cancer cells, cosmic rays’ study, to customer equity and many more approaches.

This paper presents models for evaluating effects of implementing TDM schemes on road traffic with the consideration of uncertainty of travel times. The models are composed of drivers' behavioural model for identifying the optimal starting time and route with the designated time for arrival at the destination as well as dynamic traffic simulation model for estimating the change of travel times. A case study for applying the models on a test road network showed that TDM schemes of park and ride systems and road pricing enabled drivers to obtain benefits by improving the reliability of travel times as well as benefits by reducing travel times.