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Trip distribution laws are basic for the travel demand characterization needed in transport and urban planning.
Based on these probability distribution laws, the commuting networks are simulated with different trip distribution models.
The trip purpose, OD information, trip mode and trip distribution can be efficiently obtained by this system.
However this number gives no information about the trip distribution between zones, which we can enhance by the information gained via mobile phones.
The existing urban rail network and trip distribution are major influencing factors in judging the feasibility and optimal location of the ring line.
The model introduces a time allocation step, suggests better approaches in steps such as trip distribution and mode choice, and improves the connections among each model.
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The results demonstrate that the proposed model formulation offers a great improvement in terms of root mean square error as compared with the existing taxi customer-search models, and show the model capabilities of predicting the changes in vacant taxi trip distributions with respect to the variations in the fleet size and fare.
Figure 7(b) shows that, in general terms, trip distributions and the effects still hold for CDR detected trips.
In the domain of trip distributions, new models have helped us to predict number of commuting trips when lacking data for calibration [4].
Given that the travel survey is already about five years old, it was also important to compare our results to current trip distributions.
This work arises, therefore, to investigate the presence of spatial autocorrelation in the data on the trips distribution in an urban area.
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