You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 bike_share_1
【Schema】
# Table: station
[
(id:INTEGER, Primary Key, Examples: [2, 3, 4]),
(name:TEXT, Examples: [San Jose Diridon Caltrain Station]),
(lat:REAL, Examples: [37.329732, 37.330698, 37.333988]),
(long:REAL, Examples: [-121.90178200000001, -121.888979, -121.894902]),
(dock_count:INTEGER, Examples: [27, 15, 11]),
(city:TEXT, Examples: [San Jose, Redwood City, Mountain View]),
(installation_date:TEXT, Examples: [8/6/2013, 8/5/2013, 8/7/2013])
]
# Table: status
[
(station_id:INTEGER, Examples: [2, 3, 4]),
(bikes_available:INTEGER, Examples: [2, 3, 4]),
(docks_available:INTEGER, Examples: [25, 24, 23]),
(time:TEXT, Examples: [2013/08/29 12:06:01, 2013/08/29 12:07:01, 2013/08/29 12:08:01])
]
# Table: trip
[
(id:INTEGER, Primary Key, Examples: [4069, 4073, 4074]),
(duration:INTEGER, Examples: [174, 1067, 1131]),
(start_date:TEXT, Examples: [8/29/2013 9:08, 8/29/2013 9:24, 8/29/2013 9:25]),
(start_station_name:TEXT, Examples: [2nd at South Park]),
(start_station_id:INTEGER, Examples: [64, 66, 22]),
(end_date:TEXT, Examples: [8/29/2013 9:11, 8/29/2013 9:42, 8/29/2013 9:43]),
(end_station_name:TEXT, Examples: [2nd at South Park]),
(end_station_id:INTEGER, Examples: [64, 69, 22]),
(bike_id:INTEGER, Examples: [288, 321, 317]),
(subscription_type:TEXT, Examples: [Subscriber, Customer]),
(zip_code:INTEGER, Examples: [94114, 94703, 94115])
]
# Table: weather
[
(date:TEXT, Examples: [8/29/2013, 8/30/2013, 8/31/2013]),
(max_temperature_f:INTEGER, Examples: [74, 78, 71]),
(mean_temperature_f:INTEGER, Examples: [68, 69, 64]),
(min_temperature_f:INTEGER, Examples: [61, 60, 57]),
(max_dew_point_f:INTEGER, Examples: [61, 57, 60]),
(mean_dew_point_f:INTEGER, Examples: [58, 56, 60]),
(min_dew_point_f:INTEGER, Examples: [56, 54, 53]),
(max_humidity:INTEGER, Examples: [93, 90, 87]),
(mean_humidity:INTEGER, Examples: [75, 70, 68]),
(min_humidity:INTEGER, Examples: [57, 50, 49]),
(max_sea_level_pressure_inches:REAL, Examples: [30.07, 30.05, 30.0]),
(mean_sea_level_pressure_inches:REAL, Examples: [30.02, 30.0, 29.96]),
(min_sea_level_pressure_inches:REAL, Examples: [29.97, 29.93, 29.92]),
(max_visibility_miles:INTEGER, Examples: [10, 9, 8]),
(mean_visibility_miles:INTEGER, Examples: [10, 9, 8]),
(min_visibility_miles:INTEGER, Examples: [10, 7, 6]),
(max_wind_Speed_mph:INTEGER, Examples: [23, 29, 26]),
(mean_wind_speed_mph:INTEGER, Examples: [11, 13, 15]),
(max_gust_speed_mph:INTEGER, Examples: [28, 35, 31]),
(precipitation_inches:TEXT, Examples: [0, 0.23, T]),
(cloud_cover:INTEGER, Examples: [4, 2, 6]),
(events:TEXT, Examples: [Fog, Rain, Fog-Rain]),
(wind_dir_degrees:INTEGER, Examples: [286, 291, 284]),
(zip_code:TEXT, Examples: [94107, 94063, 94301])
]

    【Question】
    Which bicycle is the least used bike. Check if the start and end station are from the same city and calculate the total duration travelled by the bicycle in hours for a trip made within the same city. least used bike refers to bike_id with MIN(COUNT(main_trip.id)); start station refers to start_station_name; end station refers to end_station_name; total duration in hour = DIVIDE(duration, 3600) AS hour;

    【Evidence】
    least used bike refers to bike_id with MIN(COUNT(main_trip.id)); start station refers to start_station_name; end station refers to end_station_name; total duration in hour = DIVIDE(duration, 3600) AS hour;

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT T2.bike_id, T2.start_station_name, T2.end_station_name, T1.city , CAST(T2.duration AS REAL) / 3600 FROM station AS T1 INNER JOIN trip AS T2 ON T1.name = T2.start_station_name GROUP BY T2.bike_id ORDER BY COUNT(T2.id) DESC LIMIT 1