Research Article

Data-Driven Approaches to Mining Passenger Travel Patterns: “Left-Behinds” in a Congested Urban Rail Transit Network

Table 1

Notations and input parameters.

SymbolDefinitions

Set of URT stations
Set of spatial nodes
Set of platform spatial nodes,
Set of spatial connections, including sections and transfer links
Set of space-time-sequence arcs
Set of walking space-time-sequence arcs
Set of left-behind space-time-sequence arcs
Set of space-time-sequence nodes
Set of activity times
Set of passengers
Index of urban railway transit stations,
Set of trains passing platform
()The train passing platform
Index of passenger,
Index of spatial nodes,
Index of time stamp,
Interval time at station at time
The station to which node belongs,
Index of spatial connection,
Index of space-time-sequence node,
Index of space-time-sequence arc indicating departing at during time interval and arriving at at during time interval,
The distribution of the time cost of space-time-sequence arc
The mean of the time cost of space-time-sequence arc
The variance of the time cost of space-time-sequence arc
The probability that a passenger leaves node at and arrives at node at
The time that a passenger should spend on
The number of times passengers who travel by are left-behind
The arrival and departure times at platform of the train,
The upper error limit of function value
The interval time of platform